Expanding management strategies for cervical precancerous lesions in resource-limited settings: insights from a training center in a district hospital in Ghana
Bibliographic record
Abstract
BACKGROUND: Cervical cancer continues to disproportionately burden women in low/middle-income countries like Ghana. We examined treatment patterns and histopathological outcomes among women screened using visual inspection with acetic acid (VIA) and/or mobile colposcopy who subsequently underwent thermal ablation, large loop excision of the transformation zone (LLETZ), or cold knife conization at the Cervical Cancer Prevention and Training Centre, Battor. We also assessed the prevalence of cervical intraepithelial neoplasia 2+ (CIN2+) or micro-invasive disease and their associated factors for women who underwent excisional treatments. The treatment choices for cervical precancerous lesions suitable for resource-limited settings have also been described from the perspective of a center that manages a heterogenous population. METHODS: We conducted an analysis of secondary data collected between June 2016 and June 2023 among women with positive findings on VIA or mobile colposcopy who subsequently underwent thermal ablation or large loop excision of the transformation zone (LLETZ). The prevalence of histopathology outcomes, including no dysplasia, CIN1 - 3, and micro-invasive disease, were estimated with 95% confidence intervals (CIs). Factors associated with histopathological findings were modeled using multinomial logistic regression. RESULTS: For the study period, 14 (10.6%) of the total 132 participants underwent cervical lesion treatment at outreach locations, all via thermal ablation. The remaining 118 (89.4%) were treated at the Catholic Hospital, Battor using LLETZ (n = 66, 55.9%), thermal ablation (n = 51, 43.2%), and cold knife conization (n = 1, 0.9%). Among 65 women with histopathology reports, the most frequent histopathological finding was no dysplasia (47.7%; 95% CI, 35.1 - 60.5), followed by CIN2 and CIN3 (20.0%; 95% CI, 11.1 - 31.8 each), CIN1 (7.7%; 95% CI, 2.5 - 17.0) and micro-invasion (4.6%; 95% CI, 1.0 - 12.9). Those with micro-invasive disease were significantly older than those with CIN1, CIN2, and CIN3 (p = 0.036, 0.022, 0.009, respectively), but not significantly older than those who showed no dysplasia (p = 0.088). For each unit increase in age, the likelihood of CIN3 was relatively significantly reduced compared to no dysplasia (crude relative risk ratio [RRR] = 0.93; 95% CI, 0.86 - 0.99). This association was neither observed with the remaining histopathological groups nor for parity and persisted after controlling for parity (adjusted RRR = 0.92; 95% CI, 0.85 - 0.99; p = 0.025). CONCLUSION: This paper largely demonstrates treatment options available to women and practitioners in LMICs. The high combined prevalence of high-grade precancerous lesions and micro-invasive disease underscores the need to increase cervical cancer awareness that would enhance screening attendance and hasten efforts at moving from opportunistic to organized screening in Ghana. This will enhance early cervical lesion detection and treatment, while simultaneously re-evaluating and cutting down on unnecessary treatment.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".