Other / unclassifiable — 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 11 August 2026 and assigned it the closest of 31 fixed categories, at 37% 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 (2 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 2 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 11 comparable posts for this entry, running 24 July 2026 to 6 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 @IPhSAIran. 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.
2 measurements taken within a single day. 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 373–375 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
7 Aug 2026, 01:18
374
no change
7 Aug 2026, 01:08
374
first reading
Engagement
11 posts held, back to 24 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 1 pageof Telegram’s post history, 20 posts per page.
ERR · 30 days
22.2%
avg views ÷ 374 subscribers
Avg views / post
82.9
11 posts measured
Reaction rate
6.94%
reactions ÷ views · ER floor
Posts in window
11
of 11 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 4 of 11 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 6 August 2026
Posts held
11 (24 July 2026 – 6 August 2026)
Views total
912
Reactions total
10
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
7 Aug 2026, 01:18 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
804
Videos
41
Links
506
Lifetime counters from Telegram’s own channel header, read 7 August 2026 — not the date at the top of this page, which is when the subscriber count was last read. Below Telegram’s rounding threshold, so these counts are exact.
Reaction mix
10 reactions across 4 posts, in 3 distinct kinds. The most used accounts for 80.0% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
8
80.0%
🔥
1
10.0%
😍
1
10.0%
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 4 of the 11 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 10reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 11 most recent posts we hold, published 24 July 2026 to 6 August 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.
✨خانواده ایفسا، خانواده ای به وسعت ایران✨
ایفسایی عزیز سلام!
همونطور که قبلا قولشو داده بودیم، به افرادی که در کارگاه آمادگی آزمون جامع ثبت نام میکنن، کد تخفیف اپلیکیشن دکتر آباد تعلق میگیره. تیم ایفسا برای دسترسی همه شما عزیزان به این کارگاه و مزایایی که داره، در سراسر کشور، در هر لوکال یک سفیر کنار شماست که کد تخفیف مخصوص لوکال شما رو در اختیارتون قرار بده تا از آزمون ها و امکانات این اپلیکیشن نهایت استفاده رو ببر…
پاینده باد مشروطه ایران
امروز ۱۴ مردادماه سالگرد صادر شدن فرمان مشروطه است.
در چنین روزی در سال ۱۲۸۵ خورشیدی فرمان مشروطیت توسط مظفرالدینشاه قاجار و پس از تلاشها و جانفشانیهای آزادیخواهان امضا و صادر شد.
یاد همه آزادیخواهان گرامی و مسیرشان پررهرو باد.
پینوشت: سند موجود در تصویر؛ نامه ستارخان به عضدالملک نائبالسلطنه
"بیرق استقلال ایران را بلند کردم ..."
انجمن دانشجویان داروسازی (ایفسا) یزد🌹
Call for the IPSF Delegation to UNESCO's Digital Learning Week
Education in the age of AI: Facts | Frictions | Frontiers
Dear IPSF Pharmily,
We are pleased to announce the call for applications for the IPSF Delegation to UNESCO Digital Learning Week 2026, to be held from the 8th to 11th of September 2026 at UNESCO Headquarters in Paris, France. This document outlines the session's focus, delegates' responsibilities…
Call for the IPSF Delegation to UNESCO's 4th Global Forum on the Ethics of Artificial Intelligence
Dear IPSF Members,
We are pleased to announce the call for applications for the IPSF Delegation to the UNESCO Global Forum on the Ethics of AI, to be held from the 14th to 17th of September 2026 in Riyadh, Saoudi Arabia.
Please note that delegates must participate in person in Riyadh, Saoudi Arabia.
● Deadline: 13 Aug…
کمیته آموزش نشنال ایفسا (تیم لوکال تهران، آزاد تهران و شهید بهشتی) با همکاری دکتر آباد برگزار میکند:
💊کارگاه آمادگی آزمون جامع 💊
دوست من سلام!
اگه دغدغهی آزمون جامع رو داری،
اگه نمیدونی مرور این همه درس رو از کجا شروع کنی،
اگه دنبال یه جمعبندی مفید و یه آزمون استاندارد برای سنجش خودتی،
💯 این کارگاه دقیقا برای توئه!
اساتید قراره دروس رو به صورت خلاصه، جمعبندیشده و کاربردی برات توضیح بدن تا هم مرور بهتری داشته…
کمیته آموزش نشنال ایفسا (تیم لوکال تهران، آزاد تهران و شهید بهشتی) با همکاری دکتر آباد برگزار میکند:
💊اساتید کارگاه آمادگی آزمون جامع 💊
ارائه دروس:
🔺فارماسیوتیکس
🔺فارماکوگنوزی
🔺بالینی
🔺شیمی دارویی
🔺و فارماکولوژی
🔸به همراه جلسه انتقال تجربه، با حضور جناب آقای دکتر راستین اخوان
❗️لینک ثبت نام❗️
❗️لینک ثبت نام دوره جامع فارماکولوژی❗️
🌱کد تخفیف ۲۵ درصدی ثبت نام زود هنگام(تا سه شنبه ساعت ۲۴) : iphsa_family
📆تاریخ ب…
🌱 سلام و عرض ادب خدمت ایفسایی های عزیز
❗️با توجه به تعویق آزمون جامع داروسازی، برنامه دوره جامع آمادگی فارماکولوژی، از ۲۰ مرداد آغاز خواهد شد.
❕ ضمنا ثبت نام در این کارگاه، تا تاریخ ۱۷ مرداد تمدید شد.
ممنون از توجه شما.
#آموزش
#نشنال
#لوکال_ایفسا_تهران
#ایفسا_خانواده_دانشجویان_داروسازی
#Be_The_Change
___
💊 Telegram | LinkedIn | Instagram
💊 PR | Group | Youtube | Castbox
🎙️ World Hepatitis Day |
وقتی آگاهی، میتواند جان انسانها را نجات دهد...
🔔 سهشنبههای سلامت با ایفسا | قسمت پنجم
گاهی بزرگترین مانع درمان، خودِ بیماری نیست؛
بلکه ناآگاهی، باورهای نادرست و نابرابری در دسترسی به خدمات سلامت است.
🎧 اینجا «سهشنبههای سلامت با ایفسا»ست؛
جایی برای شنیدن داستانهای کوتاه اما مهم از دنیای سلامت؛ ساده، علمی و قابل لمس.
📌 به مناسبت روز جهانی هپاتیت
(World Hepatitis Day)
⁉️چرا با وجود …
💡💊 آیا هر تجربه انسانی واقعاً یک بیماری یا مشکل پزشکی است، یا کمکم داریم همه جنبههای زندگی روزمره را پزشکیسازی میکنیم؟
مرز بین Medicalization (پزشکیسازی) و Medicinization (داروییسازی) باریکتر از چیزی است که فکر میکنید.
در این وبینار با ما باشید تا با هم بررسی کنیم این مفاهیم چگونه بر نظام سلامت، تصمیمهای درمانی و نگاه ما به سلامت و بیماری تأثیر میگذارند.
🎙 سخنران: دکتر نیایش محبی فارماکوتراپیست
دانشیار دا…
💡💊 Is every human experience a medical condition, or have we begun to medicalize everyday life?
The line between Medicalization and Medicinization is thinner than you might think.
Join us for an eye-opening discussion as we explore how these concepts influence healthcare, clinical practice, and the way we understand health and illness.
🎙 Speaker:
Dr. Niyayesh Mohebbi
Pharmacotherapist
Associate Professor of Clinical …
کمیته آموزش نشنال با همکاری کمیته آموزش لوکال تهران برگزار میکند:
💊 دوره جامع بازاموزی و آمادگی فارماکولوژی ۱ و ۲ و ۳ 💊
👩⚕با حضور خانم دکتر رسولی
🧪متخصص فارماکولوژی و توکسیکولوژی
✅ویژه دانشجویان داروسازی سراسر کشور
✅دوره ای کامل برای آزمون جامع داروسازی
✅آمادگی برای امتحانات فارماکولوژی
✅آنلاین و در بستر اسکای روم
⏱با ۱۲ جلسه ۱/۵ ساعته آموزش
📆از ۱۱ تا ۳۱ مرداد
⭐️لینک ثبت نام⭐️
مهلت ثبت نام تا ۶ مرداد میباشد.
…
❤2
Showing the 11 most recent of 11 posts we hold for @yphs_sca. 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.
Citation-graph rank
Citation-graph rank — 942,795 of 1,345,403entries 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
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
Names
Channels on the register whose handles appear in this channel's posts.
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 7 August 2026 — this
entry's latest reading, not the date you are reading this.
“انجمن دانشجویان داروسازی یزد - ایفسا” (@yphs_sca), 374 subscribers as measured 7 August 2026. Telegram Register, tgregister.com/channel/yphs_sca.
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.
