Misdiagnoses of Hair and Scalp Disorders in Adult Patients With Skin of Color
Notice bibliographique
Résumé
The epidemiology of hair and scalp disorders differs among patients with skin of color (SoC). Several hair and scalp disorders are more prevalent, and others are less prevalent [1, 2]. There is low representation of SoC images in medical education [3], and dermatology trainees have demonstrated lower levels of confidence in caring for patients with SoC compared to patients with White skin [4]. We systematically reviewed all published cases of misdiagnoses affecting the hair and scalp in patients with SoC to understand gaps in care and areas for improving patient management. Embase, MEDLINE, Scopus, and Web of Science were searched from inception to March 2025 with assistance from a medical librarian. Robust keywords for SoC, misdiagnosi and hair and scalp disorders were used (Supplemental Table 1, available via Mendeley at https://doi.org/10.17632/4ywngyw6rp.2). After duplicates were removed, 3272 articles were assessed in the title and abstract screening phase, and 116 in the full-text stage (Supplemental Figure 1, available via Mendeley at https://doi.org/10.17632/4ywngyw6rp.2). Articles were eligible for inclusion if they related to patients with SoC with a scalp disorder or hair who received a misdiagnosis or challenging diagnosis. Misdiagnosis was defined in situations where treatment was given for an initial incorrect diagnosis and later changed to a correct diagnosis. A challenging diagnosis was said to exist when a clinician's initially incorrect working diagnosis was changed to a correct final diagnosis. Non-English studies, peer-reviewed works such as abstracts or conference proceedings, and review articles were excluded. Twenty-one studies, reporting on 30 patients, were identified with a mean age of 31.5 years (range: 2–75) (Table 1). Most patients were female (53%). Misdiagnoses accounted for 63% of patients; the initial and final diagnoses for all patients are detailed in Figure 1. Two life-threatening misdiagnoses included T-cell lymphoblastic lymphoma and leukemia, along with malignant angioendothelioma. Other severe misdiagnoses included a patient initially diagnosed with a systemic lupus erythematosus flare, but who ultimately had neurosyphilis as the final diagnosis. Atypical disease presentations and incomplete patient work-ups were associated with misdiagnosis. In this cohort, scalp biopsies were more helpful in determining the correct diagnosis (50%) compared to trichoscopy (33%). Challenging cases comprised 11 patients, mostly females (67%) and Black (91%), with a working diagnosis of central centrifugal cicatricial alopecia (CCCA) (45%). Biopsies led to the correct diagnosis 100% of the time. Our study highlights the importance of completing a thorough history and physical examination for hair and scalp disorders in patients with SoC. Trichoscopy and scalp biopsy helped clarify unclear clinical presentations and corrected misdiagnoses. Premature diagnostic closure based on typical clinical features, such as the location of hair loss, was a source of misdiagnosis. Five Black patients who presented with vertex alopecia were initially suspected to have CCCA, given their characteristic hair loss location and pattern, and the increased CCCA prevalence among Black women. Alopecia areata was diagnosed in two of these five patients with trichoscopy. A scalp biopsy was performed in the remaining three patients, who were ultimately diagnosed with lichen planopilaris. A recent study on the prevalence and outcomes of scalp biopsies in Black women with hair loss found that biopsies resulted in a change in diagnosis 70% of the time [5]. Limitations of our study included the potential for misclassification and differences among countries, cultures, and health care access. Both reporting and publication biases may be present. Given the absence of a comparison group, our data do not enable an estimation of the prevalence of misdiagnoses in patients with SoC. Improved efforts at incorporating more SoC-specific training in medical education are warranted. Future large-scale studies assessing clinician decision-making errors may also be warranted to improve diagnostic accuracy. Dr. Jeffrey Donovan has received honoraria from Pfizer and Vichy, has participated on advisory boards at Pfizer for payment, participates on the Board of Directors for the Scarring Alopecia Foundation, has received royalties from UpToDate, and is the active Director of the Evidenced-Based Hair Training Program. The other authors have no conflicts of interest relevant to this manuscript to disclose.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».