Bibliographic record
Abstract
alopecia | alopecia areata | androgenetic alopecia | breastfeeding | decision-making | hair loss | pregnancyIt is widely recognized that hair contributes significantly to self-image and identity, and that hair loss can trigger a range of negative psychological outcomes.Less appreciated, however, is the extent to which the fear or reality of hair loss can shape patients' medical decisions.I find myself increasingly providing care to patients who made difficult decisions to refuse cancer treatments, halt reproductive plans, decline surgery, or endure treatment-related side effects in order to protect or restore their hair.These patients represent a clinically relevant and ethically complex phenomenon that medicine has yet to fully recognize.To address this gap in clinical awareness and research, I propose the "HairFirst Groups" framework-five distinct categories of patients who prioritize hair preservation above conventional medical recommendations (Table 1).With published data on this phenomenon being limited, I hope to highlight the need for continued study of hair prioritizing behaviors and the impact they have on patient decision-making.Such efforts will help us better recognize and care for patients in these groups.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.031 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".