The Optimal Organization of Gynecologic Oncology Services: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: A system-level organizational guideline for gynecologic oncology was identified by a provincial cancer agency as a key priority based on input from stakeholders, data showing more limited availability of multidisciplinary or specialist care in lower-volume than in higher-volume hospitals in the relevant jurisdiction, and variable rates of staging for ovarian and endometrial cancer patients. METHODS: A systematic review assessed the relationship of the organization of gynecologic oncology services with patient survival and surgical outcomes. The electronic databases medline and embase (ovid: 1996 through 9 January 2015) were searched using terms related to gynecologic malignancies combined with organization of services, patterns of care, and various facility and physician characteristics. Outcomes of interest included overall or disease-specific survival, short-term survival, adequate staging, and degree of cytoreduction or optimal cytoreduction (or both) for ovarian cancer patients by hospital or physician type, and rate of discrepancy in initial diagnoses and intraoperative consultation between non-specialist pathologists and gyne-oncology-specialist pathologists. RESULTS: One systematic review and sixteen additional primary studies met the inclusion criteria. The evidence base as a whole was judged to be of lower quality; however, a trend toward improved outcomes with centralization of gynecologic oncology was found, particularly with respect to the gynecologic oncology care of patients with advanced-stage ovarian cancer. CONCLUSIONS: Improvements in outcomes with centralization of gynecologic oncology services can be attributed to a number of factors, including access to specialist care and multidisciplinary team management. Findings of this systematic review should be used with caution because of the limitations of the evidence base; however, an expert consensus process made it possible to create recommendations for implementation.
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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 | 0.011 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".