Reliability of Histopathology for the Early Recognition of Fibrosis in Traction Alopecia: Correlation with Clinical Severity
Bibliographic record
Abstract
Traction alopecia (TA) is hair loss caused by prolonged pulling or repetitive tension on scalp hair; it belongs to the biphasic group of primary alopecia. It is non-scarring, typically with preservation of follicular stem cells and the potential for regrowth of early lesions especially if traction hairstyles are stopped. However, the alopecia may become permanent (scarring) and fail to respond to treatment if the traction is excessive and prolonged. Hence, the ability to detect fibrosis early in these lesions could predict patients who respond to treatment. Histopathological diagnosis based on scalp biopsies has been used as a gold standard to delineate various forms of non-scarring alopecia and to differentiate them from scarring ones. However, due to potential discrepant reporting as a result of the type of biopsy, method of sectioning, and site of biopsy, histopathology often tends to be unreliable for the early recognition of fibrosis in TA. In this study, 45 patients were assessed using the marginal TA severity scoring system, and their biopsies (both longitudinal and transverse sections) were systematically assessed by three dermatopathologists, the aim being to correlate histopathological findings with clinical staging. Intraclass correlation coefficients were used to determine the level of agreement between the assessors. We found poor agreement of the identification and grading of perifollicular and interfollicular fibrosis (0.55 [0.23-0.75] and 0.01 [2.20-0.41], respectively), and no correlation could be drawn with the clinical severity score. Better methods of diagnosis are needed for grading and for recognition of early fibrosis in TA.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".