Improving inter-observer variability in the evaluation of ultrasonographic features of polycystic ovaries
Bibliographic record
Abstract
BACKGROUND: We recently reported poor inter-observer agreement in identifying and quantifying individual ultrasonographic features of polycystic ovaries. Our objective was to determine the effect of a training workshop on reducing inter-observer variation in the ultrasonographic evaluation of polycystic ovaries. METHODS: Transvaginal ultrasound recordings from thirty women with polycystic ovary syndrome (PCOS) were evaluated by three radiologists and three reproductive endocrinologists both before and after an ultrasound workshop. The following endpoints were assessed: 1) follicle number per ovary (FNPO), 2) follicle number per single cross-section (FNPS), 3) largest follicle diameter, 4) ovarian volume, 5) follicle distribution pattern and 6) presence of a corpus luteum (CL). Lin's concordance correlation coefficients (rho) and kappa statistics for multiple raters (kappa) were used to assess level of inter-observer agreement (>0.80 good, 0.60 - 0.80 moderate/fair, <0.60 poor). RESULTS: Following the workshop, inter-observer agreement improved for the evaluation of FNPS (rho = 0.70, delta rho = +0.11), largest follicle diameter (rho = 0.77, delta rho = +0.10), ovarian volume (rho = 0.84, delta rho = +0.12), follicle distribution pattern (kappa = 0.80, delta kappa = +0.21) and presence of a CL (kappa = 0.87, delta kappa = +0.05). No improvement was evident for FNPO (rho = 0.54, delta rho = -0.01). Both radiologists and reproductive endocrinologists demonstrated improvement in scores (p < 0.001). CONCLUSION: Reliability in evaluating ultrasonographic features of polycystic ovaries can be significantly improved following participation in a training workshop. If ultrasonographic evidence of polycystic ovaries is to be used as an objective measure in the diagnosis of PCOS, then standardized training modules should be implemented to unify the approach to evaluating polycystic ovarian morphology.
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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 | 0.066 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".