Agreement between Dermatologists and Primary Care Practitioners in the Diagnosis of Malignant Melanoma: Review of the Literature
Bibliographic record
Abstract
Background: The incidence of melanoma continues to rise in the developed world. It is therefore essential for primary care practitioners (PCPs) to be able to discriminate between malignant and benign cutaneous findings, as most patients present to PCPs first for examination of suspicious lesions. Objective: To compare dermatologists and PCPs in the diagnosis of malignant melanoma. Methods: Prospective studies published from January 1950 to August 2010 in MEDLINE, EMBASE, CINAHL, and CancerLit databases were examined. Relevant medical search terms, discussed amongst the authors, were entered into the databases. Only articles comparing dermatologists and PCPs in the diagnosis of malignant melanoma were selected. Results: Dermatologists were reported as having sensitivities, specificities, and diagnostic accuracies ranging from 0.74 to 1.00, 0.56 to 0.95, and 0.85 to 0.89, respectively. PCPs had sensitivities, specificities, and diagnostic accuracies ranging from 0.25 to 0.88, 0.26 to 0.71, and 0.49 to 0.80, respectively. Conclusions: PCPs should receive more training to improve their ability in the diagnosis of malignant melanoma. Contexte: La fréquence du mélanome est en hausse continue dans les pays développés. Dans ce contexte, il est essentiel que les médecins de premier recours (MPR) puissent distinguer les lésions cutanées malignes des lésions cutanées bénignes, étant donné que la plupart des patients consultent d'abord un MPR pour l'examen des lésions douteuses. Objectif: L'étude visait à comparer les dermatologues et les MPR dans le diagnostic du mélanome malin. Méthode: Nous avons procédé à un examen d'études prospectives, publiées de janvier 1950 à août 2010, dans les bases de données MEDLINE, EMBASE, CINAHL, et CancerLit. Les termes d'interrogation médicaux pertinents, ayant fait l'objet de discussion parmi les auteurs, ont été inscrits dans les bases de données. Seuls les articles dans lesquels il y avait une comparaison entre dermatologues et MPR dans le diagnostic du mélanome malin ont été sélectionnés. Résultats: La sensibilité, la spécificité, et la précision du diagnostic parmi les dermatologues variaient de 0.74 à 1.00, de 0.56 à 0.95, et de 0.85 à 0.89, respectivement, tandis que la sensibilité, la spécificité, et la précision du diagnostic parmi les MPR variaient 0.25 à 0.88, de 0.26 à 0.71, et de 0.49 à 0.80, respectivement. Conclusion: Les MPR devraient recevoir plus de formation afin d'améliorer leur capacité de diagnostic du mélanome malin.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".