Education — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-FP8, prompt version 1) read this channel’s own recent posts on 12 September 2026 and assigned it the closest of 31 fixed categories, at 100% 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.
Growth
26 measurements spanning 94 days, net +209. 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 7,441–7,716 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 26
Measured (UTC)
Subscribers
Change
8 Nov 2026, 23:40
7,684
+8
5 Nov 2026, 14:39
7,676
+6
2 Nov 2026, 19:08
7,670
-7
30 Oct 2026, 20:07
7,677
-1
28 Oct 2026, 04:00
7,678
+7
25 Oct 2026, 04:11
7,671
+9
21 Oct 2026, 21:41
7,662
+7
18 Oct 2026, 12:15
7,655
+10
15 Oct 2026, 09:08
7,645
+12
12 Oct 2026, 15:20
7,633
+17
6 Oct 2026, 00:42
7,616
+12
28 Sept 2026, 13:57
7,604
+26
18 Sept 2026, 13:42
7,578
-2
14 Sept 2026, 10:00
7,580
+14
11 Sept 2026, 00:18
7,566
+19
5 Sept 2026, 15:22
7,547
+2
1 Sept 2026, 22:15
7,545
+6
30 Aug 2026, 08:24
7,539
+7
27 Aug 2026, 02:03
7,532
+13
24 Aug 2026, 11:44
7,519
first reading
Engagement
29 posts held, back to 22 March 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 35 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
35.4%
avg views ÷ 7,684 subscribers
Avg views / post
2,720
8 posts measured
Reaction rate
2.56%
reactions ÷ views · ER floor
Posts in window
8
of 29 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 9 November 2026
Posts held
29 (22 March 2026 – 9 November 2026)
Views total
21,740
Reactions total
557
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
12 Nov 2026, 02:37 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
453
Videos
70
Links
127
Lifetime counters from Telegram’s own channel header, read 12 November 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.
Video runtime
1m 53s
Average length
57s
Measured directly from 2 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
2,387 reactions across 29 posts, in 5 distinct kinds. The most used accounts for 60.1% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
1,435
60.1%
🔥
753
31.5%
👍
156
6.54%
🥰
39
1.63%
😇
4
0.168%
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 29 of the 29 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 2,387 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 29 most recent posts we hold, published 22 March 2026 to 9 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.
Telegram Stars
Stars received
5
across the posts below
Posts paid on
5
of 29 we hold a reading for · 17%
Most on one post
1
single highest reading
A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @CorgiUSMLE. Telegram publishes the count on the public post preview alongside ordinary reactions, and this register reads it there. It is the only figure on this site that measures money moving rather than attention.
Stars are not reactions, and the two are never added. They are rendered in the same strip on Telegram and counted in the same shape, but one is a tap and the other is a purchase. The reaction totals and the engagement rate elsewhere on this page exclude every figure in this section, and no rate here is computed against a reaction count.
This is not revenue, and we publish no currency figure. What a Star costs a reader and what it pays a channel are different numbers, Telegram takes a share we cannot observe, and the terms have changed. Converting a Star count into money would be an estimate dressed as a measurement, so the count is where we stop.
Counted over the 29 most recent posts we hold for this entry, published 22 March 2026 to 9 November 2026. Star counts above 1,000 reach us in Telegram’s short form and carry the same three-significant-figure rounding as everything else on this page.
Recent posts
Telegram’s own ‘edited’ marker: 2 of 8 posts held from the last 30 days (25.0%) 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).
Ребята, привет! Бесплатной менторской группе быть! 🥳 Спасибо за вашу активность и интерес!
Что вас ждёт?
Полтора месяца совместной подготовки к USMLE Step 1 по микробиологии, большие встречи со мной и ментором Кристиной из нашей команды (про неё выше есть отдельный пост - Кристина, спасибо, что согласилась помочь!), организованный учебный процесс и, конечно, моральная поддержка в чате. В общем, за качество ручаюсь!
…
Всем привет из Cleveland Clinic 😇
Ребята, я думаю организовать бесплатные менторские группы для Usmle step 1 с фокусом на студентов и ординаторов. Если вам интересно, пишите в комментариях. Чем больше будет отклика тем выше шанс, что мы их откроем. У нас уже был опыт пару лет назад и всем очень понравилось!
Спасибо 🤗
Что такое менторство в CorgiPrep и как оно помогает готовиться к USMLE?
Представьте корабль, который оказался в густом тумане. Вокруг ничего не видно, непонятно, куда двигаться и какие препятствия ждут впереди. В такой ситуации нужен маяк, который поможет сориентироваться и выбрать направление.
Примерно так работает менторство. Ментор — человек, который уже прошёл похожий путь, знает о возможных трудностях и помога…
Запись встречи с Иваном Стукаловым уже на YouTube 🌸
Поговорили о резидентуре по Internal Medicine и работе после неё.
Смотреть запись 🐶
Иван окончил резидентуру в Lincoln Medical and Mental Health Center в Бронксе. Сейчас работает Primary Care Physician в Mass General Brigham в Нью-Гэмпшире и подрабатывает hospitalist.
На встрече обсудили:
⚡️ Резидентуру изнутри: как адаптироваться в первые месяцы, чему учат и к…
Знакомьтесь: Кристина Довженко — ментор CorgiPrep 🐶
В этом году она закончила Сеченовский университет по специальности «Лечебное дело». В ординатуру в России решила не поступать: основная цель — резидентура в США.
Сейчас Кристина живёт в Боснии и Герцеговине, занимается документами после переезда, учит сербский, готовится к Step 2 CK и активно работает со студентами CorgiPrep.
А ещё ведёт блог о подтверждении меди…
Решаете UWorld уже несколько недель, а процент правильных ответов почти не меняется? В такой ситуации хочется решать ещё больше вопросов или заново перечитать First Aid. Но не всегда это помогает.
Поэтому собрали для вас подробный разбор: как находить причины ошибок, правильно разбирать вопросы, работать со слабыми темами и отслеживать прогресс в подготовке 👇
https://telegra.ph/CHto-delat-esli-rezultaty-UWorld-ne-r…
31 октября встречаемся с Иваном — поговорим о резидентуре по Internal Medicine и работе после неё.
Ссылка в боте @corgiprep_bot
Иван родом из Светлогорска, Беларусь. Отучился он в Гродненском государственном медицинском университете.
В 2021 году Иван поступил в резидентуру в Lincoln Medical and Mental Health Center в Бронксе, а в 2024-м окончил её. Сейчас работает Primary Care Physician в Mass General Brigham Inter…
Привет! Это CorgiPrep 🐶 Мы помогаем врачам и студентам-медикам готовиться к USMLE и двигаться к своей цели — медицинской карьере в США.
CorgiPrep — проект, который создали врачи, сами прошедшие путь подготовки к экзаменам и поступления в американскую резидентуру. Сейчас мы делимся своим опытом и помогаем другим пройти этот путь более организованно.
Что есть в нашем канале?
📚 Подготовка к USMLE — разбираем ресурсы,…
Знакомьтесь: Анастасия Оширова — ментор CorgiPrep 🐶
Настя — врач-эндокринолог из Иркутска.
Её результаты:
— Step 1 — Pass
— Step 2 CK — 245
Сейчас она готовится к OET и планирует стажировки в США в 2027 году.
Путь к USMLE у Насти тоже был не совсем прямым. Первые два месяца подготовки к Step 1 она, по её словам, героически пыталась пройти 10 страниц биохимии 😅
Потом пришла в CorgiPrep, где появился понятный план…
Привет всем 🌸
Хотим запустить новую серию встреч — разговоры с резидентами разных специальностей в США 🇺🇸
Будем приглашать врачей и обсуждать, как устроена резидентура изнутри: как проходит обычный день, чему учат, какая нагрузка, что нравится и не нравится в специальности и кому она вообще может подойти.
Ещё немного поговорим о поступлении: на что смотрят программы при отборе кандидатов и что важно для Match. А т…
Привет всем! Как заниматься регулярно, если кроме USMLE есть университет, работа, дежурства и обычная жизнь?
А что делать, если план снова не выполнен, Anki копится, а результаты тестов стоят на месте?
На эфире поговорили о том, как Сергей сам готовился к экзаменам, когда учился в университете и параллельно работал на двух работах. Обсудили, что помогало не выпадать из подготовки и как выстраивать её так, чтобы хва…
❤52🔥13👍3
Showing the 12 most recent of 29 posts we hold for @CorgiUSMLE. 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.
Stars beside a post are paid reactions — Telegram Stars, bought with money and spent on that post. They are a different unit from reactions and are never added to them, here or anywhere else on this page.
Posts edited after publishing
@CorgiUSMLE edited 2 posts after it first published — the same permalink now carries different wording than the one this register originally read, caught because our own crawl held a copy of the earlier text.
An edit is not deception. Typo fixes, price updates and corrections look exactly like this too — this register can tell you the wording changed and when, not why. How this is measured.
First edit seen
6 November 2026
Most recent edit
11 November 2026
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 2 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.
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.
USMLE Anki @USMLEAnki · 48,410 Telegram ranks this channel #22 of 71 here — alongside 70 others — read 26 August 2026
NBME CMS STEP 2 CK | USMLE @USMLENBME2CK · 74,279 Telegram ranks this channel #34 of 69 here — alongside 68 others — read 26 August 2026
MRCP ROAD - PART TWO @DrOsamaMrcpPart2 · 18,728 Telegram ranks this channel #37 of 71 here — alongside 70 others — read 5 October 2026
Usmle recalls step 1,2 &3 2025 @usmlerecallshub · 29,794 Telegram ranks this channel #38 of 77 here — alongside 76 others — read 8 September 2026
Medical USMLE @kaplanusmles · 43,614 Telegram ranks this channel #39 of 74 here — alongside 73 others — read 28 August 2026
NBME STEP 1 | USMLE 2026 @USMLE_NBME · 21,816 Telegram ranks this channel #46 of 78 here — alongside 77 others — read 22 September 2026
PATHOMA VIDEOS @pathomaavideos · 141,809 Telegram ranks this channel #52 of 56 here — alongside 55 others — read 10 September 2026
USMLE Rx Videos 2023 | USMLE STEP 1 @USMLERxStep1 · 38,844 Telegram ranks this channel #52 of 74 here — alongside 73 others — read 31 August 2026
DRPATOLOJİ 🤜 @drpatoloji · 26,146 Telegram ranks this channel #54 of 56 here — alongside 55 others — read 12 September 2026
SYNAPSE I СИНАПС @synapse_channel_1 · 40,185 Telegram ranks this channel #56 of 88 here — alongside 87 others — read 4 September 2026
Pixorize Videos | USMLE Step 1 @USMLEPixorize · 26,558 Telegram ranks this channel #59 of 82 here — alongside 81 others — read 13 September 2026
UWorld Step 2 CK QBank 2022 | USMLE WORLD @USMLEWorldStep2CK · 60,296 Telegram ranks this channel #60 of 79 here — alongside 78 others — read 26 August 2026
NBME STEP 1 | USMLE @USMLENBME · 68,801 Telegram ranks this channel #61 of 75 here — alongside 74 others — read 26 August 2026
UWorld Step 1 QBank 2024 (USMLE WORLD) @USMLEWorldStep1 · 96,426 Telegram ranks this channel #61 of 78 here — alongside 77 others — read 26 August 2026
Pathoma Videos 2024 | USMLE @USMLEPathoma · 107,985 Telegram ranks this channel #65 of 75 here — alongside 74 others — read 26 August 2026
Medbooks @supermedbook · 20,952 Telegram ranks this channel #67 of 69 here — alongside 68 others — read 28 September 2026
Mehlmanmedical channel @mehlmanmedical · 26,713 Telegram ranks this channel #67 of 78 here — alongside 77 others — read 13 September 2026
BoardsAndBeyondVideos @USMLEBoardsAndBeyondVideos · 55,919 Telegram ranks this channel #69 of 76 here — alongside 75 others — read 26 August 2026
Physeo Videos Step 1 | CK @USMLEPhyseo · 32,463 Telegram ranks this channel #70 of 84 here — alongside 83 others — read 5 September 2026
Pathoma Videos 2026 @Pathoma_Videos · 24,329 Telegram ranks this channel #71 of 74 here — alongside 73 others — read 16 September 2026
First aid 2026 🫡 @first_aid2026 · 24,860 Telegram ranks this channel #72 of 74 here — alongside 73 others — read 17 September 2026
Физиология человека @fiziologii1tj · 28,423 Telegram ranks this channel #72 of 86 here — alongside 85 others — read 16 September 2026
Reclin - live @reclinlive · 31,721 Telegram ranks this channel #73 of 88 here — alongside 87 others — read 7 September 2026
BNB 2026 @BoardsandBeyond2024 · 43,234 Telegram ranks this channel #76 of 83 here — alongside 82 others — read 30 August 2026
This channel appears in 31 seed channels' Telegram-generated recommendation lists in total, of which the 24 where it ranks highest are shown above. 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 8 November 2026 — this
entry's latest reading, not the date you are reading this.
“Корги и USMLE” (@CorgiUSMLE), 7,684 subscribers as measured 8 November 2026. Telegram Register, tgregister.com/channel/CorgiUSMLE.
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.