The role of surgeon specialty in management and survival of malignant ovarian germ cell tumors: A population-based study
Bibliographic record
Abstract
OBJECTIVES: The aim of this study is to describe management and survival in adult patients with malignant ovarian germ cell tumors (MOGCT) undergoing surgery by general gynecologists (GG) versus gynecologic oncologists (GO). METHODS: This is a population-based retrospective cohort study, including patients (age ≥ 18 years old) with MOGCT identified in the provincial cancer registry of Ontario, (1996-2020). Baseline characteristics, surgical and chemotherapy treatment were compared between those with surgery by GG or GO. Cox proportional hazards (CPH) model was used to determine if surgeon specialty was associated with overall survival (OS). RESULTS: Overall, 363 patients were included. One-hundred and sixty (44%) underwent surgery by GO and 203 (56%) by GG. There were higher rates of stage II-IV in the GO group (27.5% vs 3.9%, p < 0.001, and higher proportion of chemotherapy (64.4% vs 37.4%, p < 0.0001). Five-year OS was 90% and 93% in the GO vs GG groups, respectively (p = 0.39). CPH model showed factors associated with increased risk of death were older age at diagnosis (HR 1.09, 95% CI 1.07-1.12) and chemotherapy (HR 3.12, 95% CI 1.44-6.75). Surgeon specialty was not independently associated with all-cause death (HR 1.04, 95% 0.51-2.15, p = 0.91). CONCLUSIONS: In this group of MOGCT, 5-year OS was not significantly different between patients having surgery by GO compared to GG. Nevertheless, survival rates were lower than expected in the GG group despite their low-risk features. Further exploration is warranted regarding the reasons for this and whether patients with suspected MOGCT may benefit from early assessment by GO for optimal management.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".