Natural History of Cervical Intraepithelial Neoplasia
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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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".