Nailed Wellness: A Proposal for Training Nail Salons to Detect Signs of Organ System Dysfunction on their Client’s Nails
Bibliographic record
Abstract
The physical characteristics of one’s fingernails can indicate a large variety of diseases. Nail salon staff can be trained to recognize red flags on a client’s nails with an emphasis on safely screening clients and referring them to the appropriate healthcare providers when necessary. This proposal is based on the Save Your Skin Foundation’s successful skin cancer detection framework used in hair salons across Canada. Training consists of informational posters and short videos. This system would empower workers outside of the healthcare sector to advocate for the health of their clients while creating another tier of disease screening. ---------- Les caractéristiques physiques des ongles peuvent être un signe d’une grande variété de maladies. Le personnel des salons de manucure peut être enseigné à reconnaître les signes inquiétants sur les ongles d’un client et les orienter vers une équipe de soins de santé appropriée si nécessaire. Cette proposition est basée sur le cadre de détection du cancer de la peau de la « Save Your Skin Foundation », utilisé avec succès dans les salons de coiffure à travers le Canada. L’enseignement consiste en des affiches d’information et de courtes vidéos. Ce système permettrait aux travailleurs n’appartenant pas au secteur de la santé de défendre la santé de leurs clients tout en créant un autre niveau de contrôle des maladies.
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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.033 | 0.019 |
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".