Side effects and acceptability measures for thermal ablation as a treatment for cervical precancer in low-income and middle-income countries: a systematic review and meta-synthesis
Bibliographic record
Abstract
Objective Understanding the side effects and acceptability of thermal ablation (TA) is necessary before large-scale application in screen-and-treat programmes can be justified in low-income and middle-income countries (LMICs). Design Articles were selected for inclusion by two independent reviewers. Risk of bias was assessed using the Downs and Black’s criteria. Summary data were extracted, and authors contacted for data when necessary. Proportions of interest and 95% CIs were estimated using a random effects model. Subgroup analysis was performed based on place of treatment and timing of post-treatment follow-up. Heterogeneity was estimated using the I 2 . Eligibility criteria Studies that reported one or more side effects or patient acceptability measures after treatment of the cervix using TA in women living in LMICs who completed a cervical cancer screening test. Included articles were clinical trials or observational studies available in English and published before 18 December 2020. Information sources Ovid MEDLINE, EMBASE, CINAHL, CAB Global Health and WHO Global Index Medicus were searched for this systematic review and meta-synthesis. Results A total of 1590 abstracts were screened, 84 full text papers reviewed and 15 papers selected for inclusion in the qualitative review, 10 for meta-synthesis (N=2039). Significant heterogeneity was found in screening tests used to identify women eligible for TA and in methods to ascertain side effects. The most commonly reported side effect during treatment was pain (70%, 95% CI 52% to 85%; I 2 =98.01%) (8 studies; n=1454). No women discontinued treatment due to pain. At treatment follow-up, common side effects included vaginal discharge (72%, 95% CI 18% to 100%; I 2 =99.55%) (5 studies; n=771) and bleeding (38%, 95% CI 15% to 64%; I 2 =98.14%) (4 studies; n=856). Satisfaction with treatment was high in 99% (95% CI 98% to 100%; I 2 =0.00%) of women (3 studies; n=679). Conclusions TA results in a number of common side effects, though acceptability remains high among women treated in LMICs. Standardised side effect and acceptability reporting are needed as TA becomes more readily available. PROSPERO registration number CRD42020197605.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".