A new prognostic index model using meta-analysis in early-stage epithelial ovarian cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: To construct a novel prognostic index (PI) model of early-stage epithelial ovarian cancer (EOC).\n\nMETHODS: The PI model was constructed through meta-analyses. The methodological quality of the studies was assessed using the modified Jadad scale for randomized controlled trials (RCTs) and the Newcastle-Ottawa scale for non-RCTs. The prognosis factors of the PI model that had a significant impact on the recurrence-free survival (RFS) of patients with early-stage ovarian cancer were chosen. A total of 177 patients with early-stage ovarian cancer who were treated at Severance Hospital were analyzed using the new PI model to test its utility.\n\nRESULTS: The equation PI=2 횞 age+86 (if grade 2) or 105 (if grade 3)+53 (if stage Ib or Ic) or 130 (if stage II)+53 (if no lymphadenectomy)-43 (for adjuvant chemotherapy of 3 times or more)+10 (calibrating constant) was derived. Based on PI values, the high-risk group showed a significant 5 year-RFS difference compared to the low-risk group (P-value<0.01 by log-rank test) and a borderline significance in comparison to the intermediate-risk group (P-value=0.08). When the cutoff level of PI values was set at 211, the low- and high-risk groups of recurrence within 5 years were also identified by Cox regression analysis (HR=7.25, 95% CI: 2.98-17.65).\n\nCONCLUSIONS: Our PI model was predictive in this study and may be effective in clinical practice. Further prospective studies should be conducted to confirm the predictive ability of the new PI model for early-stage EOC recurrence.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.005 | 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 it