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For Contact
1. https://www.linkedin.com/in/mohammed-elmisiry/
2. https://www.youtube.com/@ResidentReady
Created
Between 1 November 2023 and 31 May 2024— estimated from Telegram’s id allocation, not measured. How this range is calculated.
14 measurements spanning 17 days, net -5. 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 10,598–10,606 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
24 Aug 2026, 22:19
10,599
-4
23 Aug 2026, 05:55
10,603
+2
21 Aug 2026, 20:07
10,601
+1
19 Aug 2026, 18:25
10,600
-2
18 Aug 2026, 21:47
10,602
-2
17 Aug 2026, 20:09
10,604
-1
16 Aug 2026, 21:37
10,605
+1
15 Aug 2026, 14:17
10,604
+2
12 Aug 2026, 20:04
10,602
+1
10 Aug 2026, 15:41
10,601
-1
9 Aug 2026, 17:22
10,602
+2
8 Aug 2026, 20:21
10,600
-4
7 Aug 2026, 17:01
10,604
no change
7 Aug 2026, 16:49
10,604
first reading
Engagement
16 posts held, back to 10 June 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 29 pagesof Telegram’s post history, 20 posts per page.
ERR · 30 days
8.30%
avg views ÷ 10,599 subscribers
Avg views / post
880
5 posts measured
Reaction rate
0.546%
reactions ÷ views · ER floor
Posts in window
5
of 16 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 24 August 2026
Posts held
16 (10 June 2026 – 24 August 2026)
Views total
4,399
Reactions total
24
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
25 Aug 2026, 07:43 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
99
Links
89
Lifetime counters from Telegram’s own channel header, read 25 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
54 reactions across 15 posts, in 4 distinct kinds. The most used accounts for 81.5% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
44
81.5%
🔥
8
14.8%
👍
1
1.85%
🥰
1
1.85%
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 15 of the 16 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 54reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 16 most recent posts we hold, published 10 June 2026 to 24 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.
السلام عليكم زملائي الأعزاء ❤️
الحمد لله، تم الانتهاء من طباعة كتاب الجلدية أخيرًا 📚✨
كتاب عملته بحيث يكون مرجعًا عمليًا ومفيدًا لكل:
👨⚕️ طبيب ممارس عام
👨⚕️ طبيب جلدية
وأي دكتور بيقابل حالات جلدية بشكل متكرر في العيادة أو الوحدة
الكتاب معمول بطريقة عملية تساعدك تتعرف على الحالة، توصل للتشخيص، وتتعامل معها بشكل صحيح.
📦 خلال يومين إن شاء الله هنعلن عنه
وهيكون الكتاب متاح للشحن لأي مكان داخل مصر.
ولو حابب تحجز نسخت…
الفيديو ده توعوي جدا جدا جدا ... ياريت تسمعوه
واعملوا إسقاط علي الدكاترة اللي بتاخذ التشخيص والعلاج مباشرة من الذكاء الاصطناعي وبرده اللي بيذاكر بشكل كلي دون الرجوع والقراءة من المصادر الأساسية
https://youtu.be/EUt4vyIhGoM?si=gHura-lOzTngpPo2
🔥 قبل ما تمسك الـsuture… لازم تعرف إمتى وإزاي تقفل الـwound!
هل تعرف تقيّم الـwound صح؟
هل تفرق بين أنواعها حسب mechanism & contamination؟
وهل عندك structured approach ثابت للـhistory والـexamination؟
🩸 في Approach to Wounds – Lecture 2 بنبني الـclinical approach خطوة بخطوة — مش مجرد معلومات تحفظها للامتحان، لكن طريقة تفكير تستخدمها مع المريض
https://youtu.be/bDBRMnN8UEE
يعني هتتعلم تستخدم أدوات الذكاء الاصطناعي بطريقة صحيحة تساعدك في الشغل، من غير ما تستبدل فهمك العلمي أو تفكيرك النقدي.
📌 إيه المختلف في البرنامج؟
✅ Live one-to-one mentorship.
كل متدرب هيكون عنده جلسات مباشرة وفردية، عشان نتابع مستواه والمشكلات اللي بتقابله أثناء تنفيذ المشروع.
✅ Real meta-analysis project.
مش هتتدرب على مثال افتراضي؛ هتنضم إلى مشروع بحثي حقيقي وتشارك فعليًا في تنفيذه بهدف الوصول إلى Journal sub…
الريسيرش مبقاش رفاهية للطبيب‼️
خصوصًا لو أنت ماشي لطريق أمريكا 🇺🇸 او أي دولة اوروبية
في ظل وجود ال AI النشر بقى اسهل واسرع من زمان.
لكن لسه فيه مشكلة.
كل الشرح والكورسات الموجودة بتشرح الطريق القديمة بدون ال AI !!
على ارض الواقع الناس الخبرة مش شغالين كدا!!
فيه طرق جديدة وطرق أسهل وادق كمان!
عشان كده أطلقنا في EviMentor:
🔶 Research Mentorship Program
More Than a Course!
مش مجرد محاضرات مسجلة أو شرح نظري، لكن…
الحمد لله تم الانتهاء من الفصل الخامس من كورس الممارس العام 🎉
📚 أنف وأذن وحنجرة + رمد + جلدية
✅ 16 حلقة
✅ 15 ساعة و42 دقيقة من الشرح
✅ 106 صفحة PDF عالية الجودة
وبكده بنقرب خطوة جديدة من استكمال الكورس كامل بإذن الله.
أي ملاحظات أو اقتراحات أو نقاط تحبوا نشوفها بشكل أفضل في الحلقات القادمة، ياريت تبعتولي مباشرة.
الفيدباك بتاعكم بيفرق جدًا في تطوير المحتوى.
🙏 طلب أخير:
لو شايف إن الكورس مفيد، شاركه مع زميل أو صديق…
🚨 سؤال لكل دكتور شغال طوارئ أو داخل على التكليف...
مريض دخل الاستقبال عنده أرتيكاريا منتشرة + تورم في الشفايف + نهجان بسيط...
إيه أول حاجة هتعملها؟ 🤔
❌ هتكتب ديكساميثازون؟
❌ هتدي أفيل؟
❌ ولا هتعمل "كوكتيل الطوارئ" الشهير؟
ولا...
✅ هتعتبر الحالة Anaphylaxis وتدي Adrenaline IM فورًا؟
المفاجأة إن لحد دلوقتي كتير من الأطباء بيبدأوا بالـ Dexa + Avil cocktail
رغم إن الأدلة والإرشادات الحديثة بتأكد إن:
⚠️ مضادات الهيستا…
❤8
Showing the 12 most recent of 16 posts we hold for @ResidentReady. 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.
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.
Doctor Hypo @doctorhypo · 98,191 Telegram ranks this channel #41 of 86 here — alongside 85 others — read 20 August 2026
سافر (Suffer) يا دكتور @Suffer_ya_doctor · 134,354 Telegram ranks this channel #45 of 88 here — alongside 87 others — read 16 August 2026
This channel appears in 2 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 24 August 2026 — this
entry's latest reading, not the date you are reading this.
“Resident Ready” (@ResidentReady), 10,599 subscribers as measured 24 August 2026. Telegram Register, tgregister.com/channel/ResidentReady.
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.