Symptoms Versus Impairment
Bibliographic record
Abstract
Diagnosing ADHD based primarily on symptom reports assumes that the number/frequency of symptoms is tied closely to the impairment imposed on an individual's functioning. That presumed linkage encourages diagnosis more by Diagnostic and Statistical Manual of Mental Disorders (4th ed.) style symptom lists than well-defined, psychometrically sound assessments of impairment. The current study correlated measures reflecting each construct in four separate, large-scale ADHD research samples. Average correlation between symptoms and impairment accounted for less than 10% of variance. Symptoms never predicted more than 25% of the variance in impairment. When an ADHD group was formed according to a measure of current symptoms, the sample size shrunk by 77% when a criterion-based measure of impairment was added. The partial unlinking of symptoms and impairment has implications for decisions about the diagnostic process, research criteria for participant inclusion, prevalence estimates, gender ratios, evaluation of treatment effects, service delivery, and many other issues.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".