Surgical procedure logging with use of a hand-held computer.
Bibliographic record
Abstract
OBJECTIVE: To evaluate the feasibility of incorporating hand-held computing technology in a surgical residency program, by means of hand-held devices for surgical procedure logging linked through the Internet to a central database. SETTING: Division of General Surgery, University of Toronto. DESIGN: A survey of general surgery residents. METHODS: The 69 residents in the general surgery training program received hand-held computers with preinstalled medical programs and a program designed for surgical procedure logging. Procedural data were uploaded via the Internet to a central database. Survey data were collected regarding previous computer use as well as previous procedure logging methods. MAIN OUTCOME MEASURE: Utilization of the procedure logging system. RESULTS: After a 5-month pilot period, 38% of surgical residents were using the procedure-logging program successfully and on a regular basis. Program use was higher among more junior trainees. Analysis of the database provided valuable information on individual trainees, hospital programs and supervising surgeons, data that would assist in program development. CONCLUSIONS: Hand-held devices can be implemented in a large division of general surgery to provide a reference database and a procedure-logging platform. However, user acceptance is not uniform and continued training and support are necessary to increase acceptance. The procedure database provides important information for optimizing trainees' educational experience.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".