Health & wellness — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-UD-Q6_K_XL, prompt version 1) read this channel’s own recent posts on 10 August 2026 and assigned it the closest of 31 fixed categories, at 97% confidence. This is a model’s judgement about what the channel is likely to be about, not a fact this register measured the way a subscriber count or a view count is measured — it can be revised on a later pass, and it carries no weight anywhere else on this page. How this classification works, and the other topics.
Observations
These are measurements, not verdicts. Each one below states something we counted, alongside the evidence it was counted from, so you can check it rather than take it. None of them is graded: every observation this register holds is recorded at severity 0, because the precision of the detectors behind them has not been measured yet, and a rating we cannot support is worse than none. Read each as a fact about the data, not as a judgement about the channel. How we measure.
Content that also appears on other registered channels
Posts published here appear in near-verbatim form on 12 other registered channels. This entry also sits inside a group of 151 channels linked by matching posts, directly or through other channels in the group; two members of the group may share no posts with each other. The matching is on the text itself, not on Telegram’s forward marker, so it finds a copy whether or not it was labelled as one.
Matching posts — open both and compare (6 of the pairs behind the counts below)
Channels this entry shares post bodies with, where at least one matching post pair has no forward credit between the two channels, or to a common source, recorded in our data
Text overlap is the Jaccard coefficient over the set of distinct three-word phrases in the two bodies: 1.00 is identical wording, and the threshold for counting a pair at all is 0.70. Candidates are generated by simhash LSH (4 x 16-bit bands, exact Hamming <= 3) verified against the bodies with Jaccard over the SET of distinct 3-word shingles. Published first counts which side of each matching pair carries the earlier timestamp — in this corpus, which is the limitation directly below.
What this cannot establish
MEASURED, DOMINANT ERROR SOURCE: a post ingested before 2026-08-06 may have carried a forward header that was not recorded. A 45-pair hand-check against live t.me pages found 14 (31%) where the live page shows a forward header naming the other channel and the database has none, plus 4 more (9%) naming a third party. The text match itself was wrong 0 times out of 45. Read attribution_capture.items_in_trusted_window before treating the unattributed count as a claim.
Telegram lets a channel forward a post with a header naming the source, and we only began reliably recording that header on 2026-08-06. 75 of the 75 matches recorded here fall after that date and carried no header when we read them. The rest predate reliable capture and are not evidence either way.
“Published first” means first in this corpus. We hold 599 comparable posts for this entry, running 4 August 2026 to 14 November 2026. A channel we have read one page deep will look younger than a neighbour we have read in full, and the order would flip with no change in the underlying facts.
The detector’s own notes on this observation, as it recorded them. Names in this_style are fields of the underlying evidence record, which the plain-English paragraphs above read out for this entry.
Verbatim republication has three causes and the text separates only two: a clone/mirror, unattributed copy-paste, or BOTH channels copying a common third source that neither attributes. The spread filter (content held by at most 8 channels) reduces the third and does not remove it.
'Earliest' means earliest IN THIS CORPUS. A channel ingested one page deep will look younger than a neighbour ingested in full; corpus_coverage above is there to be checked before the direction is believed.
shared_verified_est extrapolates the sampled pass rate over the full narrow match count; sampled/passed are the numbers actually measured.
Absence of a forward header is not proof of intent: Telegram lets a channel disable forward attribution, and a credit written in the body is not parsed as attribution here (mention_edge_either_way above is the closest available signal).
Of the group of 151 channels, the one whose matching posts we hold earliest is @ProfSucharitBhakdi. That channel is not among the channels listed above, so this entry may share no posts with it and this does not say where this entry’s content came from. It is a statement about our reading window, not a claim of authorship.
Recorded under the keys clone_copy · clone_mutual · clone_source, last confirmed 15 November 2026. An observation that a later pass no longer finds is cleared, and a cleared observation is removed from this page rather than being shown struck through — we do not keep publishing a claim we have withdrawn. Dispute an observation.
Channels linked by matching posts
This channel is in a group of 151 registered channels linked by matching posts, directly or through other channels in the group. This channel may share no posts with some of the channels listed below, and listed channels may share none with each other. Links are found by comparing text fingerprints across every channel on the register.
Which channel, if either, published first is deliberately not shown in this list. “First” could only mean first in our own crawl, which depends on how deep we happened to reach into each channel’s history, not on when the content was actually first posted. Our one hand-check read 45 of the highest-confidence flagged pairs against the live Telegram pages. In 18 of them the live page showed a “Forwarded from” credit that our database had no record of, so reading that pair as an unattributed copy was wrong; the check did not measure who published first. This list is ordered by subscriber count, the same as every other listing on this site, never by which channel we think came first. Near-verbatim matching has several ordinary explanations besides copying — a channel mirroring itself, an unattributed repost, or two channels independently repeating the same wire story — and this measurement cannot tell those apart. How this is measured.
100 more channels are in this same group but not listed individually here.
Growth
63 measurements spanning 100 days, net -510. Dots are measurements; the straight line between them is drawn to join them, not to claim we know the path taken in between — snapshots are recorded only when a count changes, so gaps mean “no change observed”, never “interpolated”. The vertical axis spans 26,925–27,598 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 63
Measured (UTC)
Subscribers
Change
14 Nov 2026, 10:46
27,003
-26
11 Nov 2026, 09:36
27,029
-13
9 Nov 2026, 15:00
27,042
+4
8 Nov 2026, 07:56
27,038
-13
7 Nov 2026, 04:15
27,051
-12
6 Nov 2026, 02:20
27,063
-10
4 Nov 2026, 22:14
27,073
-12
4 Nov 2026, 00:58
27,085
-4
3 Nov 2026, 00:54
27,089
-16
1 Nov 2026, 21:39
27,105
-19
31 Oct 2026, 18:28
27,124
-5
30 Oct 2026, 15:16
27,129
-4
29 Oct 2026, 13:37
27,133
-1
28 Oct 2026, 11:36
27,134
-11
27 Oct 2026, 09:27
27,145
-7
26 Oct 2026, 11:49
27,152
+1
25 Oct 2026, 09:21
27,151
-9
24 Oct 2026, 10:56
27,160
-3
23 Oct 2026, 11:57
27,163
-7
22 Oct 2026, 11:38
27,170
first reading
Engagement
697 posts held, back to 4 August 2026 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 159 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
6.67%
avg views ÷ 27,003 subscribers
Avg views / post
1,800
229 posts measured
Reaction rate
1.73%
reactions ÷ views · ER floor
Posts in window
229
of 697 held
ERR is average views per post over the last 30 days divided by subscribers, the definition TGStat uses, so this figure is comparable with the one you will see elsewhere. It falls structurally as a channel grows: a high ERR on a small channel and a low one on a large channel describe reach mathematics, not quality. We publish the figure and the sample it came from and pass no verdict on it.
ER is defined industry-wide as (forwards + reactions + comments) ÷ views — note the denominator is views, not subscribers. Telegram’s public web preview carries views and reactions but not forward or comment counts, so the reaction rate above is the reactions term only and is therefore a floor: the true ER for this channel is higher by an amount we have not measured and will not estimate. It is computed over the 228 of 229 measured posts that carry a reaction reading, and over those same posts' views.
What these figures were computed from
Window
Rolling 30 days · latest post in window 14 November 2026
Posts held
697 (4 August 2026 – 14 November 2026)
Views total
412,381
Reactions total
7,101
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
15 Nov 2026, 04:32 UTC
Views are a single reading per post, taken at the time above. A post published in the last day or two is still accumulating views, which pulls the 30-day average down slightly. That is a property of the standard definition rather than a fault in it, so we keep the definition rather than “correcting” the number into something nobody can reproduce.
Precision. Telegram publishes view counts on its public widget in short form — 8.12K, 3.7M — so any reading at or above 1,000 reaches us rounded to three significant figures, and only counts below 1,000 are exact. Averages and rates derived from them are shown to the same precision rather than to the unit: a figure like 3,701,250 would assert digits nobody measured.
Reaction counts are published per emoji and rounded the same way, so a total below 1,000 is exact and a larger one is a sum that may carry a rounded component from each emoji above 1,000. Because it is a sum, it does not look rounded — read a large reaction total as three significant figures per contributing emoji rather than as the figure it prints.
What this channel posts
Photos
≈13,700
Videos
≈9,960
Links
≈28,000
Lifetime counters from Telegram’s own channel header, read 15 November 2026 — not the date at the top of this page, which is when the subscriber count was last read. A count marked ≈ was rounded by Telegram before we ever saw it — t.me prints these counters in full below 1,000 and to three significant figures above, so ≈142,000 means somewhere between 141,500 and 142,499.
Video runtime
18h 15m
Average length
5m 59s
Measured directly from 183 videos with a duration reading, out of the posts we hold for this channel — not this channel’s whole posting history, only the sample this register has actually read. An exact reading to the second, taken from the post itself rather than from Telegram’s own rounded chrome, so it carries no ≈ mark.
Reaction mix
22,677 reactions across 676 posts, in 45 distinct kinds. The most used accounts for 22.4% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
5,087
22.4%
👍
4,849
21.4%
🔥
2,647
11.7%
💯
1,801
7.94%
🤮
1,780
7.85%
🤬
1,272
5.61%
😢
937
4.13%
😈
795
3.51%
🙏
769
3.39%
👏
521
2.30%
😭
263
1.16%
😱
259
1.14%
💩
252
1.11%
👎
167
0.736%
😡
151
0.666%
🤯
135
0.595%
🤡
122
0.538%
⚡
116
0.512%
🙈
110
0.485%
🤣
98
0.432%
25 further kinds
546
2.41%
No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.
Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.
Coverage. Reactions were read on 690 of the 697 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 23,212 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 697 most recent posts we hold, published 4 August 2026 to 14 November 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
Advertising
Ad load
0.143%
1 of 697 posts carry an ad marker
Regulatory tokens
0
none — marked by hashtag only
Median views · ads
2,270
over 1 measured post
Median views · rest
1,740
over 696 measured posts
An ad marker, not a judgement about a post. A post is counted here because it carries one of two explicit markings: an erid token, which Russian law has required on paid placements since 2022 and which is issued against a specific advertising contract, or a #реклама / #ad hashtag in the body, which is the channel declaring it itself. The first is documentary; the second is a self-declaration and is weaker. No classifier reads the text and decides — nothing on this site guesses that a post is an advertisement.
This is a floor, and it can only ever be a floor. A channel that runs paid placements without marking them produces no marker for us to count, and an unmarked ad is indistinguishable from an ordinary post on the public surface. The ad load above therefore means “the share of posts that declared themselves”, never “the share of posts that were paid for”. A low figure is not evidence of a channel that runs few ads.
Both figures are medians, and no ratio between them is published. Each is a view reading that actually occurred on a post, picked by percentile_disc rather than averaged, so one viral post cannot move it and no interpolated value is invented between two readings. The sample on one side is under five posts, which is too thin to compare. The two figures are shown side by side with the count behind each, and deliberately not divided into a headline like “ads get x% fewer views” — an arithmetic that is easy to print and, at this sample size, means nothing.
Measured over the 697 most recent posts we hold, published 4 August 2026 to 14 November 2026. Views are the latest single reading held for each post, and any reading at or above 1,000 is rounded by Telegram to three significant figures.
Recent posts
Telegram’s own ‘edited’ marker: 2 of 229 posts held from the last 30 days (0.873%) carried it when this register last read them. The marker says a post was changed after publishing, not what it said before — this register holds the earlier wording only where an edit fell between two of its own reads (see the edit ledger and how it is measured).
⚠️🧬 DR. HOTZE & DR. ANA MIHALCEA: NANOTECHNOLOGIE IM BLUT? ERSCHRECKENDE BEHAUPTUNGEN UND EIN UMSTRITTENER THERAPIEANSATZ
Was befindet sich tatsächlich im menschlichen Blut – und wie lassen sich ungewöhnliche mikroskopische Beobachtungen erklären?
In diesem Interview spricht Dr. Steven Hotze mit der Internistin Dr. Ana Mihalcea über ihre Behauptungen zu angeblichen selbstorganisierenden Nanostrukturen im Blut von ge…
MMR-Impfung: 17-mal mehr gemeldete Todesfälle als Maserntote - seit 1995
https://tkp.at/2026/11/13/mmr-impfung-17-mal-mehr-gemeldete-todesfaelle-als-maserntote-seit-1995/
𝟓. 𝐂𝐨𝐫𝐨𝐧𝐚-𝐒𝐲𝐦𝐩𝐨𝐬𝐢𝐮𝐦 𝐢𝐦 𝐁𝐮𝐧𝐝𝐞𝐬𝐭𝐚𝐠 | 𝐃𝐫. 𝐑𝐚𝐥𝐟 𝐓𝐢𝐥𝐥𝐞𝐧𝐛𝐮𝐫𝐠
Ralf Tillenburg, Facharzt für Innere Medizin und Allgemeinmedizin, war einer der Redner der Expertenrunde im 2. Block „Impfgeschädigte und Behandlung von Impfopfern“ beim 5. Corona-Symposium im Deutschen Bundestag am 6. November 2026.
Er gilt als einer der wenigen niedergelassenen Ärzte, die nachweisbare Erfolge in der Diagnostik und Behandlung von #Impfschäden…
🎄 𝐙𝐮 𝐖𝐞𝐢𝐡𝐧𝐚𝐜𝐡𝐭𝐞𝐧 𝐝𝐢𝐞 𝐒𝐭𝐢𝐦𝐦𝐞 𝐝𝐞𝐬 𝐅𝐫𝐢𝐞𝐝𝐞𝐧𝐬 𝐬𝐜𝐡𝐞𝐧𝐤𝐞𝐧
🎁 Wolfgang Borcherts Anti-Kriegs-Drama „Draußen vor der Tür“ kann heute nicht nur für Jugendliche ein sinnvolles Geschenk sein. Die Bundeswehr wird nicht müde, in Bildungseinrichtungen für ihre militärischen Laufbahnen zu werben. Wolfgang Borchert indes ist aus den Lehrplänen verschwunden.
Hier Buchtrailer ansehen 🕊️
Schwer gezeichnet von Fronterlebnissen kehrt de…
✨ Strahlen in jedem Alter – Schönheit, die von innen kommt! ✨
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In unserem nächsten Webinar der Reihe Longevity dreht sich alles um deine Schönheitsroutine: Wie können ätherische Öle und ausgewählte Supplements dein Wohlbefinden und deine natürliche Ausstrahlung unterstützen?
Mit unserem …
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…
‼️ALARMIERENDE SCHÄDEN: HPV-Impfstoff ERHÖHT HPV-Raten und Eierstockschäden bei Mädchen‼️
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Die Medicaid-Datenbank von Florida wurde analysiert, wobei geimpfte mit ungeimpften Mädchen verglichen wurden. Die Ergebnisse sind alarmierend:
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❓ Das stärkste Antioxidans fängt kein einziges Radikal
Die 7 bekanntesten Antioxidantien kennst du:
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⚡ Coenzym Q10: der Mitochondrien-Stoff für die zweite Lebenshälfte
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Alle sieben arbeiten nach dems…
Bhakdi gesteht:🚨„Ich habe jahrzehntelang Falsches gelehrt!“
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➡️ Hier geht es zur ganzen Sendung „Gesundheitslüge aufgedeckt!“: https://auf1.tv/gesundheitsluege-aufgedeckt/impfhysterie-wurde-mit-angst-eine-…
👍36❤6👏6💯2
Showing the 12 most recent of 697 posts we hold for @miteinander_mutig_sein. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
Forward network
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.
Mentions
Named by 40 registered channels — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it. The 24 listed below are the most frequent namers; the rest are counted above but not each listed.
Named by
Channels on the register whose posts name this channel's handle.
A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.
Handles this channel named that no longer answer
Dead references
3
handles named in this channel’s posts, vacant today
Evidenced gone
0
we ourselves saw one of these resolve, at some point
Never seen alive
3
vacant every time we have ever looked
@miteinander_mutig_sein named 3 handles that resolve to nothing today. That is a fact about the reference, not necessarily a fact about the handle’s history — see the two groups below.
Most of these may never have existed as a live channel at all. A handle a channel names can be a typo, an aspirational name nobody registered, or a channel that was already gone before this one ever mentioned it. Unless a row below is marked evidenced, all we know is that it references a handle that is not a live channel today — not that anything “died”. How this is measured.
Never seen alive
References a handle that is not a live channel — we have no record it ever was one.
@drbines_verbales_vitrio named in 4 posts, 4 October 2026 – 7 October 2026
@verbotene_wahrheit named in 2 posts, 26 October 2026 – 3 November 2026
@verborgene_wahrheit named in 1 post, 26 October 2026 – 26 October 2026
Channels Telegram recommends alongside this one
Telegram’s own answer, not this register’s. When this register asks Telegram’s API what is similar to this channel, this is the list it returns, in the exact order Telegram returns it — never re-sorted by subscribers or by anything else this register measures. The relationship, and the order, are Telegram’s; we record them and date them, and make no claim of our own about which of these channels actually resemble this one.
Read from Telegram’s recommendation API, most recently 11 September 2026. Telegram holds a list like this for a small and growing share of the register — how this is measured, and why most channel pages show nothing here.
Appears in Telegram’s recommendations for other channels
The reverse of the list above, and a different kind of signal. This does not require this channel to have ever been asked about directly — each row below is a channel we DID ask Telegram about, whose Telegram-generated list happened to include this one. A channel can appear here with an empty list above it, because being named by someone else’s query is independent of having been queried itself.
Rolf Kron - Ärzte stehen auf Offizieller Kanal @Rolf_Kron · 43,856 Telegram ranks this channel #7 of 80 here — alongside 79 others — read 28 August 2026
Sören Schumann - Gesundheit Ganzheitlich @soeren_schumann · 59,980 Telegram ranks this channel #10 of 69 here — alongside 68 others — read 22 August 2026
Kanal Rechtsanwalt Dirk Sattelmaier @rasattelmaier · 38,338 Telegram ranks this channel #11 of 75 here — alongside 74 others — read 31 August 2026
RA Ludwig - Querdenkeranwalt @RA_Ludwig · 62,888 Telegram ranks this channel #17 of 79 here — alongside 78 others — read 21 August 2026
Anwälte-für-Aufklärung @Anwaelte_fuer_Aufklaerung · 47,858 Telegram ranks this channel #30 of 80 here — alongside 79 others — read 26 August 2026
Die WAHRsager – Wissen bewegt! @derwahrsagerwissenbewegt · 54,564 Telegram ranks this channel #30 of 70 here — alongside 69 others — read 23 August 2026
Tom Lausen ‼️❗️original Datenanalyst @TomLausen · 45,171 Telegram ranks this channel #51 of 82 here — alongside 81 others — read 27 August 2026
MICHAEL WENDLER 🚨 & TEAM @michaelwendlerlive · 39,776 Telegram ranks this channel #60 of 69 here — alongside 68 others — read 29 August 2026
🇩🇪 Aktivist Mann & Team #Deutschland #Germany #activistman @aktivistmann · 23,348 Telegram ranks this channel #61 of 66 here — alongside 65 others — read 17 September 2026
Ignaz Bearth offiziell @ignazbearth · 32,343 Telegram ranks this channel #75 of 78 here — alongside 77 others — read 4 September 2026
▫️Rechtsanwältin Beate Bahner @rechtsanwaeltin_beate_bahner · 81,817 Telegram ranks this channel #76 of 85 here — alongside 84 others — read 18 August 2026
This channel appears in 11 seed channels' Telegram-generated recommendation lists in total. Each is Telegram’s list for THAT channel, not this one — see how this is measured.
Cite this entry
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 14 November 2026 — this
entry's latest reading, not the date you are reading this.
“Dr. med. Carola Javid- Kistel MITEINANDER MUTIG SEIN- ÄRZTE STEHEN AUF FÜR PATIENTEN.....🙏❤” (@miteinander_mutig_sein), 27,003 subscribers as measured 14 November 2026. Telegram Register, tgregister.com/channel/miteinander_mutig_sein.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.