Risk factors and comorbidities associated with central centrifugal cicatricial alopecia
Bibliographic record
Abstract
Central centrifugal cicatricial alopecia (CCCA) is the most common form of scarring alopecia that most often affects Black women. The disease typically begins with hair loss in the center scalp, which progresses in a centripetal fashion. Both environmental insult and genetics have been implicated in CCCA etiology, although the exact pathophysiology remains unknown. Nevertheless, it is important that providers feel comfortable educating their patients on risk factors (RFs) for the development or worsening of CCCA, and potential comorbidities associated with the condition. Thus, the goal of this review was to summarize these factors. A comprehensive literature search was performed, and studies were included if they reported research on RFs for or comorbidities associated with, CCCA. A total of 15 studies were included: n = 5 researching RFs for CCCA and n = 10 researching comorbidities associated with CCCA. There was an association suggesting an increased risk of CCCA with traction hairstyles in n = 2/3 studies, previous pregnancies in n = 1/1 studies, and use of chemical hair relaxers in n = 1/3 studies. Additionally, age and total years of hair loss were associated with increased CCCA severity in n = 2/2 studies. Type 2 diabetes was positively associated with CCCA in n = 3/5 studies, uterine leiomyomas in n = 1/2 studies, hyperlipidemia in n = 1/2 studies, and vitamin D deficiency in n = 1/1 studies. Conflicting results regarding RFs and comorbidities associated with CCCA exist within the literature. Thus, further investigation in larger cohorts must be done, and future research into genes implicated in CCCA and their potential role in the development of other diseases is recommended.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".