Is the International Federation of Gynecology and Obstetrics Staging System for Cervical Carcinoma able to predict survival in patients with cervical carcinoma?
Bibliographic record
Abstract
BACKGROUND: The objective of this article was to assess the clinimetric properties of the International Federation of Gynecology and Obstetrics (FIGO) staging system of cervical carcinoma to determine whether it is an adequate prognostic tool for the survival of patients with cervical carcinoma. METHODS: The FIGO staging system for cervical carcinoma was evaluated with regard to item generation, item reduction, sensibility, reliability, and validity. RESULTS: Many statistically significant and clinically important variables have been omitted from the current staging system for cervical carcinoma. The item-reduction step for the formulation of the prognostic tool has not been described by the authors of the FIGO staging system, but a consensus process is assumed. There are no studies currently available to assess the reliability of interobserver and intraobserver variability in applying the staging system to patients with cervical carcinoma. A trial to assess the reliability of this tool is proposed by the authors. Although there are no prospective trials to assess the criterion validity of the FIGO staging system, there is enough literature to suggest that the staging system is not capable of discriminating with regard to patient survival within and between stages. CONCLUSIONS: The current FIGO staging system for cervical carcinoma does not fully meet the majority of methodologic criteria for a strong predictive tool. Developing an improved prognostic index containing a complete array of independently prognostic variables is suggested.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".