A Canadian Algorithm on the Management of Telogen Effluvium
Bibliographic record
Abstract
Telogen effluvium (TE) is a form of transient hair loss in response to a triggering factor. It is a type of nonscarring alopecia, characterized by a generalized hair loss distribution with thinning of the hair and no exposed patches. It is thought to be induced by numerous stressors that cause hair follicles to suddenly switch from the growing (anagen) phase to the resting (telogen) phase. Treatment of TE consists of identifying the triggers and correcting them. TE is very common and affects men and women, with women being more emotionally and psychologically affected by the hair loss and seeking help more often. In the Canadian context, with long waiting lists to see a dermatologist, TE patients are often left alone with their worry and helplessness. This paper consolidates the opinions and recommendations of top Canadian hair-expert dermatologists with regards to TE management. Specifically, this consortium of experts identified the gap in care in Canada when it comes to TE and proposed a unified method to assess and manage TE. Their recommendations include making the proper diagnosis of TE, finding the triggers, which may include ordering appropriate investigations to identify metabolic abnormalities and nutritional deficiencies, and potentially managing treatable triggers of TE. Educating patients on TE is a crucial part of the treatment. The experts also concluded it is beneficial to offer patients a topical product that is safe and accessible.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".