#GeriMed <scp>JC</scp> : The Twitter Complement to the Traditional‐Format Geriatric Medicine Journal Club
Bibliographic record
Abstract
Twitter is a public microblogging platform that overcomes physical limitations and allows unrestricted participation beyond academic silos, enabling interactive discussions. Twitter-based journal clubs have demonstrated growth, sustainability, and worldwide communication, using a hashtag (#) to follow participation. This article describes the first year of #GeriMedJC, a monthly 1-hour live, 23-hour asynchronous Twitter-based complement to the traditional-format geriatric medicine journal club. The Twitter moderator tweets from the handle @GeriMedJC; encourages use of #GeriMedJC; and invites content experts, study authors, and followers to participate in critical appraisal of medical literature. Using the hashtag #GeriMedJC, tweets were categorized according to thematic content, relevance to the journal club, and authorship. Third-party analytical tools Symplur and Twitter Analytics were used for growth and effect metrics (number of followers, participants, tweets, retweets, replies, impressions). Qualitative analysis of follower and participant profiles was used to establish country of origin and occupation. A semistructured interview of postgraduate trainees was conducted to ascertain qualitative aspects of the experience. In the first year, @GeriMedJC has grown to 541 followers on six continents. Most followers were physicians (43%), two-thirds of which were geriatricians. Growth metrics increased over 12 months, with a mean of 121 tweets, 25 participants, and 105,831 impressions per journal club. Tweets were most often related to the article being appraised (87.5%) and ranged in thematic content from clinical practice (29%) to critical appraisal (24%) to medical education (20%). #GeriMedJC is a feasible example of using social media platforms such as Twitter to encourage international and interprofessional appraisal of medical literature.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".