Bibliographic record
Abstract
The surgical management of epithelial ovarian cancer in the South West of England was studied in the two years 1997–1998 in order to determine the factors that influence the outcome of surgery and to provide a baseline from which to assess the effect of centralisation of cancer services. All hospitals in the South West region of England participating in the Regional Cancer Organisation’s longitudinal study of outcomes in gynaecological malignancies are included. Six hundred and eighty-two patients with epithelial ovarian cancer were registered with the RCO in the two-year study period. Five hundred and ninety-five women were offered primary cytoreductive surgery of which 438 were said to be optimally cytoreduced. Applying multivariate models to analyse the outcome of surgery, older patients (OR = 0.82 per 5-year increase in age, P = 0.0003), patients treated in hospitals managing fewer than ten cases of ovarian cancer per year (OR = 1.92, P = 0.02) and patients with FIGO stage 3 (OR = 0.02, P < 0.0001) or 4 (OR = 0.002, P < 0.0001) disease were less likely to be optimally cytoreduced. Gynaecological oncologists were 2.06 times more likely to attain optimal cytoreduction when compared to general gynaecologists and this was statistically significant (P = 0.01). The results from this study support the argument that limiting surgery for ovarian malignancy to specialised surgeons improves the extent of cytoreductive surgery. © 2001 Cancer Research Campaign http://www.bjcancer.com
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.617 | 0.448 |
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".