Using the Conners' Teacher Rating Scale—Revised in School Children Referred for Assessment
Bibliographic record
Abstract
OBJECTIVE: Predictive validity of the Conners' Teacher Rating Scale-Revised (CTRS-R) was evaluated against a semi-structured clinical teacher interview in school children referred for diagnostic assessment of attention-deficit hyperactivity disorder (ADHD). We hypothesized that extreme scale values would increase diagnostic certainty and that classification errors would be associated with comorbid conditions. METHOD: Children (n = 1038), aged 6 to 12 years, were screened using the CTRS-R and their teachers were interviewed. Three levels of T scores on the 3 Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) subscales of the CTRS-R were compared with DSM-IV symptom thresholds by interview. Where subscale scores and interviews showed highest agreement, presence of comorbid disruptive behavioural disorders, reading disability, language impairment, and low IQ were investigated for children classified correctly, compared with incorrectly. RESULTS: T scores of 60 and above on all CTRS-R DSM-IV subscales offered high sensitivity, from 91% to 94%. Only on subscales M (hyperactive-impulsive) and N (total) did T scores of less than 60 offer posttest probabilities of less than 10%, confirming that a child does not reach diagnostic threshold by interview. T scores of 80 and more offered high specificity, from 88% to 93%, but did not provide high posttest probabilities that children reach diagnostic criteria. Classification errors were associated with more language impairment among false positives than true positives on the M (18.9%, compared with 11.3%, P = 0.04) and N (19.0%, compared with 9.5%, P = 0.023) subscales, and more reading disabilities among false positives than among true positives on the N subscale (35.2%, compared with 21.6%, P = 0.009). CONCLUSIONS: The ability of the CTRS-R to predict whether clinically referred children reach DSM-IV criteria for ADHD at school is limited.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".