International Microsurgery Club: An Effective Online Collaboration System
Bibliographic record
Abstract
BACKGROUND: This study aimed to determine if International Microsurgery Club (IMC) is an effective online resource for microsurgeons worldwide, in providing an avenue for timely group discussions and advice regarding complicated cases, and an avenue for collaboration and information sharing. METHODS: All posts on the IMC Facebook group from member 1 to 8,000 were analyzed according to inclusion criteria and categorized into three categories-case discussion, question, and information sharing. Posts were retrospectively analyzed for number of responses, time of responses, number of "likes," number of treatment options, time of day, and demographics of authors and responders. RESULTS: A retrospective analysis of 531 cases showed an average response rate of 75.7% within 1 hour and as membership grew. The response rate stabilized averaging between 72.5 and 78% across all times of the day. An average of 11.8 microsurgeons was involved per case discussion, and 5.7 treatment options were provided per case. CONCLUSION: IMC is shown to be an effective resource to allow microsurgeons to access timely advice from other microsurgeons without time and distance limitation, and to have interactive group discussions on complicated cases.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".