Re: Cervical Intraepithelial Neoplasia Outcomes After Treatment: Long-term Follow-up From the British Columbia Cohort Study
Bibliographic record
Abstract
The results reported by Melnikow et al. ( 1 ) are consistent with what we have observed in a similar ongoing survey. We considered 1667 women with cervical intraepithelial neoplasia 2 or 3 (CIN 2/3; 718 with CIN 2 and 949 with CIN 3; mean age = 37 years). The CIN was detected in the Florence screening program from January 1, 1985, through June 30, 2005, and was conservatively treated with conization (n = 733), loop electrosurgical excision procedure (n = 900), or local destructive treatment (diathermy or laser vaporization; n = 34). Linkage to the Tuscany Cancer Registry ( 2 ) allowed us to identify patients with invasive carcinoma that occurred at least 6 months after CIN 2/3 treatment but before December 31, 2005. Nine patients with incident cervical carcinoma were identified during a total of 16 784.29 person-years and an average follow-up of 10 years, corresponding to an invasive cancer rate of 53.6 per 100 000 women-years. Such an incidence rate was higher than that of the general population, the difference being statistically significant, with an observed to expected ratio of 5.7 (= 9/1.6, 95% confidence interval [CI] = 2.9 to 10.9). In a multivariable analysis, the incidence rate of invasive cancer was not associated with age, CIN grade, or conservative treatment type, with the highest difference being associated with local destructive treatment (odds ratio = 5.5, 95% CI = 0.46 to 66.5) compared with conization.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".