For most fully alcohol-attributable diagnoses in the ICD, the etiological specification should be removed
Bibliographic record
Abstract
Alcohol use is a risk factor for many chronic disease conditions, over 40 of which are regarded as fully alcohol-attributable. The practice of specifying the etiological cause in the names of diseases fully alcohol-attributable in the International Classification of Diseases has resulted in affected-individuals being stigmatized, misdiagnosed, and mistreated. Additional outcomes of this practice may include delayed care and misreporting. The consequences of specifying alcohol in the names of diseases causally linked to alcohol are discussed with respect to two examples: alcoholic liver disease and foetal alcohol syndrome. With respect to symptomatology and treatment, each of these conditions are not unique from there non-alcohol attributed counterparts--that is, non-alcoholic liver diseases and idiopathic neurodevelopmental disorders. Specifying “alcohol” in the name of a disease has a number of negative consequences, yet no apparent benefits. Given that the International Classification of Diseases is the international standard for reporting diseases and health conditions, having diagnostic categories that impede the ability of health care professionals to report diseases accurately is self-defeating.
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 | 0.006 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.012 |
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".