Expanding management strategies for cervical precancerous lesions in resource-limited settings: insights from a training center in a district hospital in Ghana
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».