Development and implementation of a consensus-derived synoptic operative report for Roux en Y gastric bypass surgery
Bibliographic record
Abstract
Background Operative reports (OR) are critical documents in the medical record but are often poor quality. Synoptic reporting (SR) is a potential solution. This has never been assessed in bariatric surgery. The objective was to design and trial a SR for Roux en Y gastric bypass (RYGB), a common bariatric procedure. Methods Systematic review and meta-analysis of comparative studies on SR and narrative reporting (NR) was conducted. A Delphi group developed quality indicators (QIs) for RYGB OR based on these findings. A national needs assessment and audit was then performed and a RYGB SR was subsequently designed and prospectively trailed against NRs. Results Meta-analysis found SRs more complete and efficient than NRs. Seventy-five QIs were established and found bariatric ORs of mediocre quality. RYGB SR was more complete for all items compared to NRs (>99% vs 64%). Conclusion SR for RYGB is superior to NR and should be implemented into clinical practice across Canada.
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 | 0.552 | 0.559 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".