Telementoring: An Important Enabling Tool for the Community Surgeon
Bibliographic record
Abstract
This study evaluated the efficacy of telementoring as an enabling tool for community general surgeons to perform advanced laparoscopic surgical procedures. We present a series of 19 patients who underwent advanced laparoscopic surgical procedures in two community hospitals, between November 2002 and July 2003, by four community surgeons with no formal advanced laparoscopic training. Each surgeon was telementored by an expert surgeon from a tertiary care hospital. Telementoring was achieved with real-time two-way audio-video communications over Internet Protocol or Integrated Services Digital Network lines with bandwidths from 385 kbps to 1.2 mbps. The procedures included 10 bowel resections, 5 Nissen fundoplications, 2 splenectomies, 1 reversal of a Hartmann procedure, and 1 ventral hernia repair. Two of the 19 procedures (11%) were converted to open. There were no intraoperative complications and two postoperative complications (11%). The primary surgeon considered telementoring useful in all cases (median score, 4 of 5). The mentor was also comfortable with the quality of the laparoscopic surgery performed (median score, 4 of 5). Telecommunication bandwidth for audio and video transmission was found to be a critical factor in the quality of telementoring process. Telementoring is safe and feasible. It allows community surgeons with no formal advanced laparoscopic training to benefit from expert intraoperative advice during the performance of advanced laparoscopic procedures. It may also reduce health-care costs by avoiding the need to refer and transfer patients to tertiary care centers.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".