Dual Diagnosis or Dual Confusion: Limitations When Utilizing Non-specialist Clinical Data
Bibliographic record
Abstract
In this paper, we review challenges encountered when utilizing clinical data collected from non-specialists about persons with intellectual disabilities and psychiatric disorders. We draw on our experience with a dataset of adult inpatients and outpatients drawn from the nine psychiatric hospitals in Ontario (see Lunsky et al., 2003). First we discuss the problem of identifying patients with intellectual disabilities based on standard information collected in hospitals. Many hospitals categorize their patients by primary or secondary diagnosis only, so that a co-existing intellectual disability may or may not be recorded. Then we discuss problems related to the accuracy of psychiatric diagnoses made by individuals with limited training with the population. We found major differences in diagnostic patterns between specialized dual diagnosis programs and more generic programs. We conclude the paper with the argument that it is important to study dual diagnosis not just within the population of persons with intellectual disabilities but also within the broader mental health system. Doing so, however, can lead to problems as such data has its limitations. Dual diagnosis generally refers to persons with intellectual disabilities and mental health disorders. The majority of dual diagnosis research is published in intellectual disability journals and is either descriptive in nature or is a comparison of intellectually disabled people with and without mental health problems. There are significantly fewer studies that compare individuals with dual diagnosis to individuals who have psychiatric disorders but no intellectual disabilities. Such research is essential because it allows us to directly compare symptoms, patient characteristics and treatment outcomes of those with dual diagnosis to the general population with mental health
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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 | 0.178 | 0.469 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.020 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".