Visual-Numeric Endometriosis Scoring System (VNESS) for mapping surgical findings: A validation study.
Bibliographic record
Abstract
Objective The aim of this study was to assess inter-rater reliability for components of the Visual-Numeric Endometriosis Scoring System (VNESS). VNESS is a novel system for describing surgical findings in each compartment of the pelvis in a way that is simple to use, visually intuitive and mirrors a laparoscopic image of the pelvis. Design Validation study using short surgical laparoscopic video clips. Setting Anonymized video clips of endometriosis surgery scored by 50 Gynaecologists from 12 countries. Population Gynaecologists as scores of short surgical clips selected from a series of endometriosis procedures performed between 2012 and 2022. Methods 50 gynaecologists scored 93 short surgical videoclips using VNESS. 4650 scores were compared against a reference score and analysis was performed to assess inter-rater reliability. Main outcome measures The outcome measures were percentage agreement between given and reference scores, as well as intra-class correlation coefficients (ICC), Cohen Kappa and Quadratic Weighted Kappa Coefficients were calculated to evaluate inter-rater reliability. Results The highest and lowest percentage agreement with the reference score was seen in VNESS 4 (full thickness disease, 97% perfect agreement) and VNESS 1 (superficial disease, 53% perfect agreement) respectively. The intraclass correlation coefficient showed strong inter-rater reliability for all VNESS compartments except the vagina. Conclusions This study suggests that VNESS has excellent inter-observer reliability. Correlation is stronger with more severe disease.
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".