DSM-IV, ICD-10 and FTND: Discordant Tobacco Dependence Diagnoses in Adult Smokers
Bibliographic record
Abstract
There are several measures that attempt to assess tobacco dependence but the most appropriate measure to use is often unclear. Tobacco dependence was assessed, in Canadian adult smokers of black African descent, with three measures: the Diagnostic and Statistical Manual of Mental Disorders Fourth Edition (DSM-IV), the International Classification of Diseases (ICD-10) and the Fagerström Test for Nicotine Dependence (FTND). The different measures resulted in very different tobacco dependence diagnosis rates: 91% were dependent by DSM-IV, 48% were dependent by ICD-10 and 48% were dependent by FTND (score ≥3). Although ICD-10 and FTND had the same diagnosis rates, they did not identify the same individuals as dependent (i.e., 35% of those dependent by ICD-10 were not dependent by FTND). For exploratory purposes, the dichotomous measures, DSM-IV and ICD-10, were scored as continuous scale measures, DSMC and ICDC. ICDC had the strongest agreement with FTND (ICC=0.76), followed by DSMC with ICDC (ICC=0.28) and DSMC with FTND (ICC=0.19). These exploratory analyses illustrate some limitations and strengths of the DSM-IV, ICD-10 and FTND. Moreover, we illustrate how measurement architecture and population specific variation may contribute to discordant diagnoses.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".