Web based pathology assessment in RTOG 98-04
Bibliographic record
Abstract
AIMS: Radiation Therapy Oncology Group 98-04 sought to identify women with ‘good risk’ ductal carcinoma in situ (DCIS) who receive no significant benefit from radiation. Enrolment criteria excluded close or positive margins and grade 3 disease. To ensure reproducibility in identifying good risk pathology, an optional web based teaching tool was developed and a random sampling of 10% of submitted slides were reviewed by a central pathologist. METHODS: Submitting pathologists were asked to use the web based teaching tool and submit an assessment of the tool along with the pathology specimen form and DCIS H&E stained slide. Per protocol pathology was centrally reviewed for 10% of the cases. RESULTS: Of the 55 DCIS cases reviewed, three had close or positive margins and three were assessed to include grade 3 DCIS, therefore 95% of DCIS cases reviewed were correctly graded, and 89% reviewed were pathologically appropriate for enrolment. Regarding the teaching tool, 13% of DCIS cases included forms that indicated the website was used. One of these seven who used the website submitted DCIS of grade 3. CONCLUSIONS: Central review demonstrates high pathological concordance with enrolment eligibility, particularly with regard to accurate grading. The teaching tool appeared to be underused.
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 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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".