Informatics and Ovarian Cancer Care
Bibliographic record
Abstract
Ovarian cancer affects 2,400 women annually in Canada with a case fatality ratio of 0.70. There are several practice guidelines that indicate women with early stage ovarian cancer should be appropriately staged including removal of the gynecologic organs, multiple peritoneal biopsies, and pelvic and paraaortic lymphadenectomy. In advanced disease, removing as much disease as possible and leaving less than a centimeter of residual disease in any one area improves overall duration of survival in cohort studies. Single institution studies and now work using administrative datasets in many high resource countries, show that women are not receiving adequate surgical staging or debulking. Cancer Care Ontario has used the RAND approach for defining quality indicators as a step for evaluating quality of care for several cancers including the management of women with ovarian cancer. The difficulty with current administrative datasets in the province is the lack of specific information such as stage, grade, histology, and size of residual disease. In this chapter, we will elaborate on the research that has brought ovarian cancer care to this juncture. We will highlight the importance of gathering information at the point of procedures and specifically in ovarian cancer at the point of the operation. Problems with the operative note and mechanisms to overcome these using templates, checklists, and synoptic notes will be reviewed. We will provide examples of pilot studies in Canada using synoptic operative notes in Cancer Care Alberta and Princess Margaret Hospital. We will also provide examples of computerized data entry across the spectrum of care from three projects in Ontario, Canada. Issues in building a disease site-specific electronic medical record will be discussed. The problems experienced in attempting to generalize such a system provincially will be addressed. We will elaborate on the potential benefits to the individual patient, the hospital and the province from such information system.
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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 | 0.005 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".