Training Family Medicine Residents in Dermoscopy Using an e-Learning Course: Pilot Interventional Study
Notice bibliographique
Résumé
BACKGROUND: Skin cancers are the most common group of cancers diagnosed worldwide. Aging and sun exposure increase their risk. The decline in the number of dermatologists is pushing the issue of dermatological screening back onto family doctors. Dermoscopy is an easy-to-use tool that increases the sensitivity of melanoma diagnosis by 60% to 90%, but its use is limited due to lack of training. The characteristics of "ideal" dermoscopy training have yet to be established. We created a Moodle (Moodle HQ)-based e-learning course to train family medicine residents in dermoscopy. OBJECTIVE: This study aimed to evaluate the evolution of dermoscopy knowledge among family doctors immediately and 1 and 3 months after e-learning training. METHODS: We conducted a prospective interventional study between April and November 2020 to evaluate an educational program intended for family medicine residents at the University of Montpellier-Nîmes, France. They were asked to complete an e-learning course consisting of 2 modules, with an assessment quiz repeated at 1 (M1) and 3 months (M3). The course was based on a 2-step algorithm, a method of dermoscopic analysis of pigmented skin lesions that is internationally accepted. The objectives of modules 1 and 2 were to differentiate melanocytic lesions from nonmelanocytic lesions and to precisely identify skin lesions by looking for dermoscopic morphological criteria specific to each lesion. Each module consisted of 15 questions with immediate feedback after each question. RESULTS: In total, 134 residents were included, and 66.4% (n=89) and 47% (n=63) of trainees fully participated in the evaluation of module 1 and module 2, respectively. This study showed a significant score improvement 3 months after the training course in 92.1% (n=82) of participants for module 1 and 87.3% (n=55) of participants for module 2 (P<.001). The majority of the participants expressed satisfaction (n=48, 90.6%) with the training course, and 96.3% (n=51) planned to use a dermatoscope in their future practice. Regarding final scores, the only variable that was statistically significant was the resident's initial scores (P=.003) for module 1. No measured variable was found to be associated with retention (midtraining or final evaluation) for module 2. Residents who had completed at least 1 dermatology rotation during medical school had significantly higher initial scores in module 1 at M0 (P=.03). Residents who reported having completed at least 1 dermatology rotation during their family medicine training had a statistically significant higher score at M1 for module 1 and M3 for module 2 (P=.01 and P=.001). CONCLUSIONS: The integration of an e-learning training course in dermoscopy into the curriculum of FM residents results in a significant improvement in their diagnosis skills and meets their expectations. Developing a program combining an e-learning course and face-to-face training for residents is likely to result in more frequent and effective dermoscopy use by family doctors.
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 | 0,004 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 ».