Straight to the Point: What Do We Know So Far on Hair Straightening?
Bibliographic record
Abstract
BACKGROUND: Hair represents a valued aspect of human individuality. The possibility of having an easy to handle hairstyle and changing it from time to time promoted an increasing search for chemical hair transformations, including hair straightening. Hair straightening is the process used to convert curly into straight hair. AIMS: This review aims to discuss hair straightening, addressing techniques, products, methods of application, consequences to hair shafts, recommendations on this topic, and the risks involving the safety of both the user and the performing professional. METHODS AND RESULTS: The terms "straightening" AND "hair," "straightening" AND "alopecia," and "straightening" AND "human hair" were used to perform a literature search in MEDLINE through PubMed until July 15, 2020. We limited the search to articles available in English, considering those mentioning alternatives to straighten the hair. We had a total of 33 relevant articles. CONCLUSIONS: This article will help dermatologists to advise their patients, providing a more suitable orientation on how to get the best outcome without risking one's safety.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".