Experiences with thermal ablation for cervical precancer treatment after self‐collection <scp>HPV</scp>‐based screening in the <scp>ASPIRE</scp> Mayuge randomized trial
Bibliographic record
Abstract
Cervical cancer remains a significant public health burden in low-resourced countries. Thus, the WHO prioritized cervix screening, and recently recommended thermal ablation treatment for cervical precancer. However, there is limited information on side effects during treatment and recovery, and acceptability among those treated. The ASPIRE Mayuge trial recruited women to participate in self-collection cervix screening between 2019 and 2020 (N = 2019). Screen-positive women (N = 531, 26.3%) were referred for visual inspection with acetic acid and thermal ablation treatment, per Uganda Ministry of Health recommendations; 71.2% of those referred attended follow-up. Six months post-screening, a subset of trial participants were recontacted. Those who received thermal ablation completed a survey assessing side effects during and after the procedure, and willingness to recommend the treatment to others. We summarized the results to describe the side effects and acceptability of thermal ablation treatment. Of 2019 participants, 349 (17%) received thermal ablation. A subset of 135 completed the follow-up survey, where 90% reported pain during treatment; however, intensity and duration were low. Over a third of women reported problems with recovery for reasons including pain, discharge and bleeding. Regardless, 98% reported they would recommend the treatment to others. The use of thermal ablation to treat cervical precancer appears to be highly acceptable in this population. While many women reported side effects during the procedure and recovery, the majority said they would recommend the treatment to others. However, given the substantial proportion who reported problems with recovery, efforts should be made to provide additional resources to women after receiving thermal ablation treatment for cervical precancer.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".