[Alcohol Dependence: one or more nosological entities? Identifying geographical diversities among patients through DSM-4 and Artificial Intelligence].
Bibliographic record
Abstract
BACKGROUND: although there is an overabundance of terms that over time have connoted the phenomenon usually defined as Alcoholism, or Alcohol Dependence, this has been universally considered a unitary disease by experts, the media and lay people. OBJECTIVES: to investigate whether there is a variety of configurations of alcohol dependence and alcohol abuse corresponding to different countries. DESIGN: secondary analysis of a dataset from a European project conducted in 2013-2014. The analysis was carried out mainly based on both artificial intelligence and statistical tools: Self Organizing Map (SOM) Neural Network, Multidimensional Scaling Population Algorithm (MSPA), and K-Means clustering algorithm. SETTING AND PARTICIPANTS: 1,767 participants in a European project treated in specialist alcohol dependence units in 9 countries/regions of Europe. MAIN OUTCOME MEASURES: patients were interviewed to ascertain their correspondence to the 11 DSM-4 symptoms used for the diagnosis of 'alcohol dependence' and 'alcohol abuse', following up on a previous study that used traditional statistical methodology. RESULTS: results confirm that the symptoms of 'alcohol dependence' and 'alcohol abuse' identified by the DSM-4 are distributed according to distinct European geographical macroareas: Central-Northern and Southern, with an intermediate macroarea. These could partly correspond to different typologies of drinking cultures in the different macroareas. CONCLUSIONS: alcoholism does not appear to be a unitary disease, but varies according to different cultures and social contexts, and should be treated appropriately.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".