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 98% 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 why it has no browse page of its own yet.
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 word for word on 1 other registered channel. 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)
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. None of the 6 matches recorded here fall after that date, so for this entry we cannot say whether any of them carried a credit. The duplication is measured; the absence of attribution is not.
“Published first” means first in this corpus. We hold 14 comparable posts for this entry, running 20 July 2026 to 7 August 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).
Across the whole group of 2, the earliest publisher we hold is @psarayedaneshjo. That is a statement about our reading window, not a claim of authorship.
Recorded under the key clone_copy, last confirmed 7 August 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.
Also posting the same content
This channel’s posts match, word for word or near enough, posts on 1 other registered channel, found by comparing text fingerprints across every channel on the register. That matching has been checked by hand against the live Telegram pages and found reliable — 0 wrong of 45 pairs re-read.
Which channel, if either, published first is deliberately not shown. The same hand-check found that reading wrong 18 of 45 times — 60%, no better than a coin flip — because it depends on how deep our own crawl happened to reach into each channel’s history, not on when the content was actually first posted. This list is ordered by subscriber count, the same as every other listing on this site, never by which channel we think came first. Word-for-word 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.
9 measurements spanning 8 days, net -14. 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 12,556–12,575 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
14 Aug 2026, 17:15
12,558
-10
13 Aug 2026, 06:19
12,568
-2
12 Aug 2026, 06:22
12,570
-1
11 Aug 2026, 05:16
12,571
+4
10 Aug 2026, 05:53
12,567
-2
9 Aug 2026, 03:53
12,569
-4
8 Aug 2026, 00:17
12,573
+2
6 Aug 2026, 21:58
12,571
-1
6 Aug 2026, 13:32
12,572
first reading
Engagement
33 posts held, back to 20 July 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 20 pagesof Telegram’s post history, 20 posts per page.
ERR · 30 days
5.37%
avg views ÷ 12,558 subscribers
Avg views / post
675
30 posts measured
Reaction rate
—
this channel exposes no reaction counts
Posts in window
33
of 33 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.
What these figures were computed from
Window
Rolling 30 days · latest post in window 14 August 2026
Posts held
33 (20 July 2026 – 14 August 2026)
Views total
20,246
Reactions total
—
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
15 Aug 2026, 12:45 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
≈1,050
Videos
≈688
Links
≈142
Lifetime counters from Telegram’s own channel header, read 15 August 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
44s
Average length
11s
Measured directly from 4 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.
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✅ اشتراک رسمی ۱۸ ماهه و اختصاصی شما
✅ امکان استفاده از آنتی گرویتی (Antigravity)
⚡️ فعالسازی کمتر از 1 دقیقه
🛡 همراه با ضمانت و پشتیبانی قیمت فقط 585 هزار تومان
👈 خرید:
@SquirrelAccount_bot
@SquirrelAccount_bot
👀 هنوز با آمپولهای لاغری کار نمیکنی⁉️
⚠️چون دوزینگ و پروتکلهاش رو بلد نیستی!
✨ترند این روزهای مارکت زیبایی✨
✔️آموزش صفر تا صد آمپولهای لاغری از دوزینگ و پروتکلها تا مدیریت عوارض و رژیم غذایی و...(مونجارو، اسپارتینا، اوزمپیک و...)
🌐 همین الان عضو کانال شو 👇
https://t.me/+pbBoqzZfe4piOTNk
⭕️هشدار درباره «خوددرمانی دیجیتال»؛ هوش مصنوعی مرجع تجویز دارو نیست
⏺سخنگوی سازمان غذا و دارو با هشدار نسبت به گسترش پدیده «خوددرمانی دیجیتال» تأکید کرد که استفاده از اینترنت و هوش مصنوعی برای دریافت اطلاعات سلامت، نباید به تصمیمگیری مستقل درباره مصرف دارو منجر شود. به گفته او، هوش مصنوعی میتواند ابزاری مفید برای آموزش و افزایش آگاهی باشد، اما جایگزین پزشک و داروساز در تشخیص، تجویز، تغییر یا قطع دارو نیست.
⏺تصمیم…
پروژههای پزشکی رو به هر کسی نسپار! 🩺
کلی از دانشجوهای پزشکی و پیراپزشکی بهمون پیام دادن که برای انجام پروژه، مقاله، پایاننامه و کارهای تخصصی رشتهشون دنبال یه فرد مطمئن و کاربلد بودن؛ اما سراغ افراد نامناسب رفتن و در نهایت هم هزینه و زمانشون از دست رفته.
برای همین بعد از بررسی، یک فرد مورد تأیید ما رو به دانشجوهای گروه پزشکی معرفی میکنیم؛ کسی که میتونید با خیال راحت برای پروژهتون باهاش در ارتباط باشید.
🩺 پزشک…
«هر بیمار که با درد معمولی قفسهٔ سینه (typical chest pain) به اورژانس میآید
و به او PPI و مسکن دادی، ولی دردش خوب نشد،
لطفاً او را به خانه نفرست، حتی اگر نوار قلب و تروپونین نرمال بودند.»
❓چرا؟
چون طبیعی بودن ECG و تروپونین در ابتدای مراجعه، بهطور قطعی ACS/MI را رد نمیکند.
یعنی به زبان ساده:
بیمار با درد قفسه سینه تیپیک (Typical chest pain) مراجعه کرده ⬅️ بنابراین شک بالینی به ACS بالاست.
وECG طبیعی
به این معن…
♻️استخدام در بیمارستان ،داروخانه ها و آزمایشگاه های خصوصی و دولتی با بالاترین حقوق و مزایا
@medjobpro
♻️لذت داروشناسے ونسخه خوانے رایگان
@pharrm_2018
♻️آموزش کامل تفسیر نوار قلب از مبتدی تا پیشرفته
@daneshe_parastari
♻️با ما نسخه پیچ حرفه ای شوووو
@medi_pharm
♻️استخدام (تکنسین دارویی و کارآموز داروخانه)
@niazpharma
♻️فایل #رایگان کتاب ونمونه سوالات تمام رشته های پزشکی، پرستاری، ازمایشگاهی، دارو
@pdffille
♻️❣ج…
Signed هانیه
Showing the 12 most recent of 33 posts we hold for @EmergencyNursee. 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.
Polls
The poll we hold for this entry, as Telegram rendered it when we read the post. A poll’s figures keep moving after that, so each one is dated.
Shares as published, totalling 101%. No per-option vote count is published by Telegram, so none is shown.
Percentages only — there are no per-option vote counts here, because Telegram publishes none.The public post preview gives each option’s share and a single voter total, and nothing else. Multiplying one by the other would produce a per-option tally that looks measured and is not: the shares are rounded to whole numbers before we ever see them. We print what was published and leave the column that does not exist empty.
The shares need not add up to 100.Rounding alone puts many polls at 99 or 101. A poll that allows more than one answer per voter runs well past 100 by design, and several here do. The bars are drawn against a fixed 100% track at each option’s own percentage rather than normalised to the total, so a poll that exceeds it shows that it does instead of being quietly rescaled.
Read from the 33 most recent posts we hold, published 20 July 2026 to 14 August 2026. Telegram labels each poll by kind — an anonymous poll, a quiz, a closed set of final results — and that label is reproduced rather than paraphrased.
Citation-graph rank
Citation-graph rank — 670,727 of 1,478,351entries in the measured graph. A weighted position computed from the forward and mention edges below — republished posts weigh more than named mentions — and recomputed periodically, over the whole graph. Published only as this ordinal position, never as a score: a position is a fact, and a score printed beside one channel’s name would read as a verdict this register does not make. The two counts beneath stay separate for the same reason mentions are never summed with forwards anywhere else on this page — a named-by count costs nothing to manufacture. The top 100 by this measure, or how it is computed.
Forward network
Republishes
Channels on the register whose posts this channel has forwarded.
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 24 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.
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.
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 August 2026 — this
entry's latest reading, not the date you are reading this.
“نکات مهم اورژانس🚑” (@EmergencyNursee), 12,558 subscribers as measured 14 August 2026. Telegram Register, tgregister.com/channel/EmergencyNursee.
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.
