Prognostic factors for women with Stage 1 ovarian cancer with or without adhesions.
Bibliographic record
Abstract
OBJECTIVE: To identify those prognostic factors in women with Stage 1 epithelial ovarian cancer that predict survival. METHODS: A population-based cohort study was conducted which included all newly diagnosed ovarian cancer patients treated initially with surgery from 1996-1998 in Ontario, Canada (N = 1,341). We abstracted charts from hospitals and cancer centres and used hospital and billing claims databases. Cox survival analysis was used to model the association between prognostic factors (including patient characteristics, surgical findings, pathologic findings and subsequent treatment) and survival for those with Stage 1 ovarian cancer. RESULTS: 327 women had Stage 1 or 2 ovarian cancer (where Stage 2 was based on adhesions alone). Prognostic factors that had significant, unadjusted, association with survival were patient age, presence or absence of adhesions, grade, and surface involvement. The multivariable model that best described survival included premenopausal age group (HR 0.32, 95% CI, 0.18-0.55), poor differentiation (HR 2.17, 95% CI, 1.33-3.51), and surface capsule involvement (HR 2.97, 95% CI, 1.59-5.55). A lack of influence of treatment modality stands in contrast to the literature. CONCLUSIONS: Our dataset confirmed that poor grade and surface capsule involvement are poor prognostic factors. Adjuvant therapy did not confer an improved outcome; however, it was likely used in only those patients with poor prognostic indicators and so improved their survival to that of women with good prognostic factors who received surgery alone.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".