Validation of an Algorithm for the Diagnosis of Serous Tubal Intraepithelial Carcinoma
Bibliographic record
Abstract
It has been reported that the diagnosis of serous tubal intraepithelial carcinoma (STIC) is not optimally reproducible on the basis of only histologic assessment. Recently, we reported that the use of a diagnostic algorithm that combines histologic features and coordinate immunohistochemical expression of p53 and Ki-67 substantially improves reproducibility of the diagnosis. The goal of the current study was to validate this algorithm by testing a group of 6 gynecologic pathologists who had not participated in the development of the algorithm (3 faculty and 3 fellows) but who were trained in its use by referring to a website designed for the purpose. They then reviewed a set of microscopic slides, which contained 41 mucosal lesions of the fallopian tube. Overall consensus (≥4 of 6 pathologists) for the 4 categories of STIC, serous tubal intraepithelial lesion (our atypical intermediate category), p53 signature, and normal/reactive was achieved in 76% of the lesions, with no consensus in 24%. Combining diagnoses into 2 categories (STIC versus non-STIC) resulted in an overall consensus of 93% and no consensus in 7%. The κ value for STIC versus non-STIC among all 6 observers was also high at 0.67 and did not significantly differ, whether for faculty (κ=0.66) or fellows (κ=0.60). These findings confirm the reproducibility of this algorithm by a group of gynecologic pathologists who were trained on a website for that purpose. Accordingly, we recommend its use in research studies. Before applying it to routine clinical practice, the algorithm should be evaluated by general surgical pathologists in a community setting.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".