Twitter as a tool for communication and knowledge exchange in academic medicine: A guide for skeptics and novices
Bibliographic record
Abstract
Twitter is a tool for physicians to increase engagement of learners and the public, share scientific information, crowdsource new ideas, conduct, discuss and challenge emerging research, pursue professional development and continuing medical education, expand networks around specialized topics and provide moral support to colleagues. However, new users or skeptics may well be wary of its potential pitfalls. The aims of this commentary are to discuss the potential advantages of the Twitter platform for dialogue among physicians, to explore the barriers to accurate and high-quality healthcare discourse and, finally, to recommend potential safeguards physicians may employ against these threats in order to participate productively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.015 | 0.026 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.011 |
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 source (direct Gemma or distilled Codex), 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".