Error rating tool to identify and analyse technical errors and events in laparoscopic surgery
Bibliographic record
Abstract
BACKGROUND: Surgical error analysis is essential for investigating mechanisms of errors, events and adverse outcomes. Furthermore, it provides valuable information for formative feedback and quality control. The aim of the present study was to design and validate a technical error rating tool in laparoscopic surgery. METHODS: The framework consisted of nine task groups and four error modes. Unedited videos of laparoscopic Roux-en-Y gastric bypass procedures were rated and analysed. The Objective Structured Assessment of Technical Skill (OSATS) global rating scale was used to assess technical skills. The incidence of errors and of injuries (events) were the main outcome measures, and were used to calculate the reliability, and construct and concurrent validity of the instrument. RESULTS: Two observers analysed 25 procedures. Inter-rater reliability was high regarding total number of errors (intraclass correlation coefficient (ICC) 0·90) and events (ICC 0·85). The median (interquartile range) error rate was 35 (26-44) and the event rate 3 (2-3) per procedure. Error frequencies and OSATS scores correlated significantly in all operative steps (rs = -0·75 to -0·40, P = <0·001-0·046). Surgeons demonstrating high OSATS scores had lower median (i.q.r.) error rates than surgeons with low scores in three of four steps: measuring bowel (4 (2-7) versus 10 (9-11); P = 0·004), jejunojejunostomy formation (5 (2-6) versus 10 (9-11); P = 0·001) and pouch formation (4 (3-6) versus 9 (5-12); P = 0·004). CONCLUSION: The proposed error rating tool allows an objective and reliable assessment of operative performance in laparoscopic gastric bypass procedures.
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 | 0.030 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".