Channel
@medicine_pathology
On this record: Growth · Engagement · Posts · Polls · Citations · Cite this entry
29subscribers
+6 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1003852785801 |
|---|---|
| Type | Channel |
| Username | @medicine_pathology |
| Created | Between 1 February 2026 and 30 May 2026 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 11 August 2026 |
| Last confirmed live | 14 September 2026 |
| Measurements held | 6 |
| Confirmed unchanged | 1 time, most recently 14 September 2026 |
| On Telegram | t.me/medicine_pathology |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 14 Sept 2026, 06:18 | 29 | +1 |
| 6 Sept 2026, 18:56 | 28 | +2 |
| 30 Aug 2026, 00:34 | 26 | +2 |
| 22 Aug 2026, 11:18 | 24 | +1 |
| 11 Aug 2026, 11:46 | 23 | no change |
| 6 Aug 2026, 12:32 | 23 | first reading |
20 posts held, back to 30 May 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 page of Telegram’s post history, 20 posts per page.
Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 20 posts for this entry, the most recent from 11 August 2026. An engagement rate over an empty window would be a number about nothing.
Which of the following structures, depicted in Figure, is commonly found in the sinusoidal spaces of peribronchial and mediastinal lymph nodes involved by sarcoidosis?
Shares as published. No per-option vote count is published by Telegram, so none is shown.
🔬 Clinical Case Challenge 📚 Source: Prometric — Anatomic Pathology MCQs 📝 Which of the following structures, depicted in Figure, is commonly found in the sinusoidal spaces of peribronchial and mediastinal lymph nodes involved by sarcoidosis? ━━━━━━━━━━━━━━━ 🎓 Medicine Hub — 465,000+ board questions with full explanations, images and analytics. 🌐 hub.medicine.ac • #Quiz #Pathology_Quiz
🔬 The Osler Clinical Pathology 2026 Subscription-Based Review 📝 Course Overview: Online Medical Course · 2026 The Osler Clinical Pathology 2026 Subscription-Based Review Laboratory-medicine board review · ~42 hours · released 2026 Overview A comprehensive, case-based review of clinical pathology and laboratory medicine designed around the CP certification exam. The course... 💙 Edition: 2026 📁 Category…
Which of the following adaptive changes is exemplified in the illustration?
Shares as published, totalling 101%. No per-option vote count is published by Telegram, so none is shown.
🔬 Clinical Case Challenge 📚 Source: Prometric — Clinicial Pathology MCQs 📝 The illustration shows a section of the heart from a 45-year-old African-American man with long-standing hypertension who died of a “stroke.” Which of the following adaptive changes is exemplified in the illustration? ━━━━━━━━━━━━━━━ 🎓 Medicine Hub — 465,000+ board questions with full explanations, images and analytics. 🌐 hub.medicine.ac • …
🔬 BoardVitals Bioanalysis (ABB) QBank 2025 📝 Course Overview: BoardVitals Bioanalysis (ABB) QBank 2025 A high-yield, exam-style interactive online resource for Bioanalysis (ABB). Train with realistic items, rapid explanations, and a smooth UI engineered for long study sessions on any device. Masterclass Overview of the BoardVitals Bioanalysis (ABB) QBank... 💙 Edition: 2025 📁 Category: #BoardVitals #Pathology #Qbank…
🔬 Memorial Sloan Kettering Cancer Center: The Pathology of Neoplastic Diseases 2026 📝 Course Overview: MEMORIAL SLOAN KETTERING • APR 27 – MAY 1, 2026 • PATHOLOGY OF NEOPLASTIC DISEASES Memorial Sloan Kettering Cancer Center: The Pathology of Neoplastic Diseases 2026 Memorial Sloan Kettering Cancer Center: The Pathology of Neoplastic Diseases 2026 — A focused,... 💙 Edition: 2026 📁 Category: #Pathology #MSKCC 👇 Tap…
🔬 Memorial Sloan Kettering Cancer Center: The Pathology of Neoplastic Diseases 2026 📝 Course Overview: MEMORIAL SLOAN KETTERING • APR 27 – MAY 1, 2026 • PATHOLOGY OF NEOPLASTIC DISEASES Memorial Sloan Kettering Cancer Center: The Pathology of Neoplastic Diseases 2026 Memorial Sloan Kettering Cancer Center: The Pathology of Neoplastic Diseases 2026 — A focused,... 💙 Edition: 2026 📁 Category: #Pathology #MSKCC 👇 Tap…
🔬 2025 Jason L. Hornick, M.D., Ph.D. Expert Series – Practical Soft Tissue Pathology 📝 Course Overview: 2025 Jason L. Hornick, M.D., Ph.D. Expert Series – Practical Soft Tissue Pathology Contemporary, case-driven soft tissue pathology training built around a practical, pattern-based diagnostic workflow—focused on reducing uncertainty, avoiding pitfalls, and translating modern WHO-era classification into real sign-ou…
🔬 USCAP Pathology of the Breast and Female Genital Tract 2025 📝 Course Overview: Course Description As major subspecialty practices, diagnostic breast and gynecological pathology involves a large number of disease entities and often complex histological variants. Recent developments, particularly molecular genetic discoveries, have introduced new and reclassified entities as well as refined... 💙 Edition: 2025 📁 C…
🔬 Classic Lectures in Pathology: Soft Tissue Pathology 2025 📝 Course Overview: About This CME Teaching Activity This CME Activity offers a practical review of soft tissue and bone pathology combining basic to advanced techniques, pearls and pitfalls to pathologic diagnosis and case reviews. Target Audience This CME activity is intended... 💙 Edition: 2025 📁 Category: #Board #Classic_Lectures #Edusymp #Pathology 👇…
🔬 Elizabeth A. Montgomery, M.D. Expert Series - Gastrointestinal Pathology on Cruise Control: Navigating from the Esophagus to the Anus 2025 📝 Course Overview: About This CME Teaching Activity This CME teaching activity is designed to provide a practical review of current topics in gastrointestinal pathology. Dr. Elizabeth Montgomery discusses basic to advanced topics, pitfalls and pearls to gastrointestinal patholo…
Showing the 12 most recent of 20 posts we hold for @medicine_pathology. 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.
The 2 polls we hold for this entry, as Telegram rendered them when we read the post. A poll’s figures keep moving after that, so each one is dated.
Which of the following structures, depicted in Figure, is commonly found in the sinusoidal spaces of peribronchial and mediastinal lymph nodes involved by sarcoidosis?
Shares as published. No per-option vote count is published by Telegram, so none is shown.
Which of the following adaptive changes is exemplified in the illustration?
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 20 most recent posts we hold, published 30 May 2026 to 11 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.
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.
Named by 1 registered channel — 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.
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 September 2026 — this entry's latest reading, not the date you are reading this.
“Pathology Resources | Medicine.ac” (@medicine_pathology), 29 subscribers as measured 14 September 2026. Telegram Register, tgregister.com/channel/medicine_pathology.
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.