Social media for international surgical skills transfer: using pneumatic retinopexy as a model
Bibliographic record
Abstract
OBJECTIVE: To assess social media as a potential method of bridging the gap between randomized controlled trial evidence and the implementation of pneumatic retinopexy (PnR). DESIGN: Cross-sectional study. PARTICIPANTS: Vitreoretinal surgeons from a Telegram chat group that was initiated in May 2020 for educating ophthalmologists about PnR. METHODS: Between July 25, 2023, and September 25, 2023, longitudinal chat usage was recorded using an automated chatbot, and a subgroup of surgeons was surveyed about the effect of the Telegram group on their PnR practice. RESULTS: Telegram group membership increased from 43 members in May 2020 to 885 members in June 2024, with representation from 64 different countries. A subset of 653 members sent a mean (SD) of 2.6 (28.2) messages and were active 56.4 (195.0) times between July and September 2023. Eighty-one surgeons from 35 different countries completed the survey. Between the year before and year after joining the Telegram group, the proportions of surgeons that treated >25% of RRD cases with PnR (8.6% vs 44.4%; p < .001) and self-reported >80% primary anatomical reattachment rate (27.2% vs 48.1%; p < .001) increased. CONCLUSIONS: Social media may be leveraged to support the refinement of surgical techniques such as PnR for physician trainees and practicing surgeons, as well as increase surgical adoption into routine practice, a process that can otherwise take several decades. A randomized implementation trial that compares social media to other dissemination and implementation strategies while incorporating effectiveness and safety outcomes is warranted.
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 | 0.019 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".