Natural History of Cervical Intraepithelial Neoplasia
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
OBJECTIVE: To determine the probabilities of transition of stages in the cervical cancer by conducting a meta-studies on the topic. STUDY DESIGN: We identified health states of interest in the natural history of cervical precancer, identified all possible papers that could meet selection criteria, developed relevance and acceptability criteria for inclusion, then thoroughly reviewed the selected studies. To determine the transition probability data we used a random effects model. We determined probabilities for 4 health state transitions. The 6-month mean predictive transition probability (95% confidence intervals with "prediction interval" in parentheses) for high grade squamous intraepithelial lesions (HSIL) to cancer was 0.0037 (0.00004, 0.03386), for low grade squamous intraepithelial lesions (LSIL) to HSIL was 0.0362 (0.00055, 0.23220), for HSIL to LSIL was 0.0282 (0.00027, 0.35782), and for LSIL to normal was 0.0740 (0.00119, 0.42672). CONCLUSION: The transition probabilities between cervical cancer health states for 6-month intervals are small; however, the cumulative risk of cervical cancer is significant. Markers to identify the cervical precursors that will lead to the transition to cervical cancer are needed.
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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) | 0.003 | 0.001 |
| 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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