Clinical and forensic signs related to ethanol abuse: a mechanistic approach
Bibliographic record
Abstract
For good performance in clinical and forensic toxicology, it is important to be aware of the signs and symptoms related to xenobiotic exposure since they will assist clinicians to reach a useful and rapid diagnosis. This manuscript highlights and critically analyses clinical and forensic imaging related to ethanol abuse. Here, signs that may lead to suspected ethanol abuse, but that are not necessarily related to liver disease are thoroughly discussed regarding its underlying mechanisms. This includes flushing and disulfiram reactions, urticaria, palmar erythema, spider telangiectasias, porphyria cutanea tarda, "paper money skin", psoriasis, rhinophyma, Dupuytren's contracture, multiple symmetrical lipomatosis (lipomatosis Lanois-Bensaude, Madelung's disease), pancreatitis-related signs, black hairy tongue, gout, nail changes, fetal alcohol syndrome, seborrheic dermatitis, sialosis and cancer.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".