DataKey.txt – description of data fields; UKCTOCS TVC audit data biprobit format-0.csv; UKCTOCS TVC audit data biprobit format-0.dta; UKCTOCS TVC audit data do file.do.
Bibliographic record
Abstract
Background: We report on a unique audit of seven sonographers self-reporting high visualization rates of normal postmenopausal ovaries in the United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS). This audit was ordered by the trial’s Ultrasound Management Subcommittee after an initiative taken in 2008 to improve the quality of scanning and the subsequent increase in the number of sonographers claiming very high ovary visualisation rates. Methods: Seven sonographers reporting high rates (>89%) of visualizing normal postmenopausal ovaries in examinations performed between 1 st January and 31 st December 2008 were identified. Eight experts in gynaecological scanning reviewed a random selection of exams performed by these sonographers and assessed whether visualization of both ovaries could be confirmed (cVR-Both) in the examinations. A random effects bivariate probit model was fitted to analyse the results. Results: The eight experts reviewed images from 357 examinations performed on 349 postmenopausal women (mean age 60.0 years, range 50.2-73.3) by the seven sonographers. The mean cVR-Both obtained from the model for these sonographers was 67.2% with a range of 47.6-86.5% (95%CI 63.9-70.5%). The range of cVR-Both between the experts was 47.3-88.3% and the intra-class correlation coefficient (ICC) for left and right ovary confirmation was 0.39. Conclusions: The audit suggests that self-reported visualization of postmenopausal ovaries is unreliable, as visualisation of both ovaries could not be confirmed in almost a third of examinations. The agreement for visualization of both ovaries based on review of a static image between experts and sonographers and between expert reviewers alone was only moderate. Further research is needed to develop reliable Quality Control metrics for transvaginal ultrasound.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.224 | 0.300 |
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".