The Diagnosis and Treatment of Nail Disorders
Bibliographic record
Abstract
BACKGROUND: Nail disorders can arise at any age. About half of all nail disorders are of infectious origin, 15% are due to inflammatory or metabolic conditions, and 5% are due to malignancies and pigment disturbances. The differential diagnosis of nail disorders is often an area of uncertainty. METHODS: This review is based on publications and guidelines retrieved by a selective search in PubMed, including Cochrane reviews, meta-analyses, and AWMF guidelines. RESULTS: Nail disorders are a common reason for derma - tologic consultation. They are assessed by clinical inspection, dermatoscopy, diagnostic imaging, microbiological (including mycological) testing, and histopathological examination. Some 10% of the overall population suffers from onychomycosis, with a point prevalence of around 15%. Bacterial infections of the nails are rarer than fungal colonization. High-risk groups for nail disorders include diabetics, dialysis patients, transplant recipients, and cancer patients. Malignant tumors of the nails are often not correctly diagnosed at first. For subungual melanoma, the mean time from the initial symptom to the correct diagnosis is approximately 2 years; this delay is partly responsible for the low 10-year survival rate of only 43%. CONCLUSION: Evaluation of the nail organ is an important diagnostic instrument. Aside from onychomycosis, which is a common nail disorder, important differential diagnoses such as malignant diseases, drug side effects, and bacterial infections must be considered.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".