Management of Cervical Neoplasia: A 13-Year Experience with Cryotherapy and Laser
Bibliographic record
Abstract
OBJECTIVE: To evaluate whether cryotherapy is as effective as laser therapy in treating cervical intraepithelial neoplasia (CIN), and to determine the optimal time for follow up. MATERIALS AND METHODS: Patients with biopsy-proven CIN were treated with cryotherapy or laser therapy. Specific data, including grade of CIN, rate of recurrence, and time to recurrence, were compared between the groups. RESULTS: From 2240 eligible patients, 1126 were treated with laser and 1114 with cryotherapy. Ninety-two percent of patients in the laser group and 91.6% in the cryotherapy group had no evidence of CIN after a median follow up of 60 months. The 183 patients with recurrent/persistent disease were retreated with the same treatment modality as initially received. Eighty-seven of the 90 (96.7%) patients retreated with laser and 90 of the 93 patients (96.8%) retreated with cryotherapy had no further evidence of CIN. The majority (128 out of 183; 75.4%) of recurrent/persistent disease was detected within 18 months after treatment. CONCLUSIONS: CIN can be treated with similar success by cryotherapy or laser ablation. Optimal follow up would be two years for CIN1 lesions and five years for CIN2/3 lesions.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".