Internet based second opinion pathology in a large chemotherapy trial for ovarian cancer – results of a standardized review process
Bibliographic record
Abstract
Background: It has been suggested that specialized pathology review prior to randomization should become standard procedure in study protocols, because a considerable number of patients in clinical trials of ovarian carcinoma may have histopathological diagnoses in conflict with inclusion criteria. We hypothesized that our new, internet-based high throughput infrastructure would be capable of providing specialized second opinion pathology within 10 days. Design: Patients scheduled for the AGO OVAR17 ovarian carcinoma chemotherapy trial were registered for expert pathologic case review prior to randomization. All original slides were requested from local pathologist, scanned and uploaded to a secured internet server. Five pathologists specialized in gynecologic pathology from Austria, Switzerland and Germany were available online. If necessary, immunohistochemistry was available through a collaborating pathology lab. Results: 880 patients with an original diagnosis of ovarian epithelial carcinoma were registered through our internet platform for second opinion pathology review from 10/2011 – 07/2013. In 2.5% (n = 22) of cases, a major diagnostic discrepancy of potential clinical relevance was found leading to exclusion from the chemotherapy trial. Ovarian borderline tumors and ovarian metastasis were the leading discrepant diagnosis. The average time from patient registration until completion of pathology review was 5.2 days. Conclusion: Our results show that the use of a new internet-based infrastructure makes specialized case review prior to patient randomization feasible within less than 10 working days. Our new approach might further improve quality of patient care through minimization of overtreatment, especially of patients with ovarian borderline tumors and inadequate treatment of patients with ovarian metastases.
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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 | 0.021 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".