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
Abstract Carcinoma of the cervix accounts for approximately 20% of all gynecologic cancers and 2% of all malignancies in women. Numerous prognostic factors have been studied in patients with cervical carcinoma, using both univariate and multivariate analysis. Differences in endpoints of analysis, whether survival (disease‐free or overall), relapse‐free rate, or local control rate, make comparison of such studies difficult. The failure to perform a multivariate analysis, or the use of different covariates in multivariate analyses, can further complicate comparisons between studies. Not all factors are relevant to all patients; for example, depth of tumor invasion and presence of vascular‐space invasion can only be reliably determined in patients treated with surgery, whereas hemoglobin level is important in patients treated with radiation. This review concentrates largely on those factors identified using multivariate techniques, such as log rank or Cox regression analysis, in order to account for interactions between various factors. Where available, estimates such as hazard ratios will be included in order to indicate the strengths of the individual variables.
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.
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 | 0.000 |
| 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) | 0.087 | 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