Quality indicators for operative reporting in transanal endoscopic surgery
Bibliographic record
Abstract
Transanal endoscopic surgery (TES) platforms have become quite popular. Many surgeons across the country have begun excising rectal lesions using these platforms; however, the perioperative decision-making surrounding these excisions can be quite variable. To facilitate care between providers, it would be helpful to standardize the way TES is reported. Synoptic operative reports have previously been established as an effective and efficient communication tool. For patients with rectal cancer, synoptic reports are required for pathology, radiology and major oncologic resections, but never previously for TES. We used a Delphi process including 15 stakeholders from across Canada to develop a TES synoptic report. Participants submitted items according to 6 categories: team characteristics, patient demographics, preoperative work-up, lesion characteristics, procedure details and postoperative details. Twenty-six surgeon-entered and 41 auto-populated items reached final inclusion. This will allow generation of a synoptic reporting template to improve perioperative communication for these patients.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".