Realizing the potential of real-time clinical collaboration in maternal–fetal and obstetric medicine through WhatsApp
Bibliographic record
Abstract
BACKGROUND: This study aimed to explore the potential of using instant messaging to enhance patient-care and physician-education in obstetric medicine and maternal-fetal medicine. METHODS: This retrospective study examined real-time correspondence between a closed group of maternal-fetal medicine physicians and fellows-in-training. Correspondence was grouped into four domains. Time to obtain a response and their utility was analysed. RESULTS: Over the two-year period, 41 international members contributed 534 clinically relevant messages (291 stems and 243 responses). Of these, 33% were advice seeking, 23.4% case-sharing, 35% educational content and 8.2% miscellaneous content. The median response time was 52 min, and 53% responded in less than 60 min. At least one response in each case influenced clinical management. CONCLUSION: Instant messaging is effective for real-time clinical collaboration and could serve as an important platform for enhancing management and continuing education for obstetric medicine and maternal-fetal medicine physicians. International societies should consider exploring this avenue further.
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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 | 0.011 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".