Elucidation of Molecular Alterations in Precursor Lesions of Ovarian Serous Carcinoma
Bibliographic record
Abstract
Abstract : The objectives are to 1) elucidate the pathogenesis of ovarian cancer by characterizing the early lesions, and 2) to provide biomarkers for early ovarian cancer detection. Both goals will be facilitated by this Consortium Development plan which is a collaborative, interdisciplinary program that will establish the infrastructure to coordinate research. The research sites include Johns Hopkins University, Toronto University, Memorial Sloan Kettering Cancer Center, and Yale University. The Coordination center will have three Cores. The specific goals to be accomplished are briefly summarized. The Administrative Core will collect IRB protocols from all research sites, organize the Consortium symposium including the Pathology/ Epidemiology consensus meeting, setup and test the audio-video broad band electronic device for e-conference, organize the Internal Advisory Board. The Pathology/Epidemiology Core will define criteria for early ovarian cancer lesions and select cases/controls, survey available cases and controls from all research sites, create one overall database for specimens with clinical and epidemiologic data, establish tissue trafficking mechanisms. The Biostatistics Core will establish the data collection, storage and security system, and perform statistics support in study design. The completion of these tasks is considered critical for us to continue our research in the coming Consortium research program.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.003 | 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".