Assessment of ultrasonographic features of polycystic ovaries is associated with modest levels of inter-observer agreement
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: There is growing acceptance that polycystic ovaries are an important marker of polycystic ovary syndrome (PCOS) despite significant variability when making the ultrasound diagnosis. To better understand the nature of this variability, we proposed to evaluate the level of inter-observer agreement when identifying and quantifying individual ultrasonographic features of polycystic ovaries. METHODS: Digital recordings of transvaginal ultrasound scans performed in thirty women with PCOS were assessed by four observers with training in Radiology or Reproductive Endocrinology. Observers evaluated the scans for: 1) number of follicles >/= 2 mm per ovary, 2) largest follicle diameter, 3) ovarian volume, 4) follicle distribution pattern and 5) presence of a corpus luteum (CL). Lin's concordance correlation coefficients and kappa statistics for multiple raters were used to assess inter-observer agreement. RESULTS: Agreement between observers ranged from 0.08 to 0.63 for follicle counts, 0.27 to 0.88 for largest follicle diameter, 0.63 to 0.86 for ovarian volume, 0.51 to 0.76 for follicle distribution pattern and 0.76 to 0.90 for presence of a CL. Overall, reproductive endocrinologists demonstrated better agreement when evaluating ultrasonographic features of polycystic ovaries compared to radiologists (0.71 versus 0.53; p = 0.04). CONCLUSION: Inter-observer agreement for assessing ultrasonographic features of polycystic ovaries was moderate to poor. These findings support the need for standardized training modules to characterize polycystic ovarian morphology on ultrasonography.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it