An Evaluation of a Computer-Based Psychiatric Assessment: Evidence for Expanded Use
Bibliographic record
Abstract
The purpose of this study was to examine the psychiatric diagnoses of depression made using the structured interview, the Computer-Based Diagnostic Inventory Schedule for Children-Revised (CDISC-R) and diagnoses of depression made by pediatric psychiatrists. One hundred and twenty-two adolescents who were admitted to an inpatient psychiatric treatment unit agreed to participate in the study. All participants completed the CDISC-R structured diagnostic interview and independent measures reflecting depressive symptoms. The admitting pediatric psychiatrists' diagnoses were also recorded. Even though there were more females in the sample, males (n = 38) and females (n = 84) had similar results. The computer-based CDISC-R and physician diagnoses agreed in 76% of the cases. These results were confirmed by the independent measures of depressive symptoms, which were higher for those with diagnoses of depression and lower for those without depression. In the 24% of the cases, where the CDISC-R and physician diagnoses disagreed, the computer-based CDISC-R was more accurate in assigning a diagnosis of depression in terms of the independent measures of depressive symptoms. The CDISC-R, a computer-based diagnostic interview, efficiently and precisely diagnoses depression. This finding indicates that the use of computer-based diagnostic interviews in applied research will provide more objective and precise results, especially in clinical trials. It follows from these findings that computer-based diagnostic interviews could have important clinical applications and play a central role in web-based mental health and Telemedicine by facilitating triage, referral, and monitoring treatment outcomes through remote electronic assessment.
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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 | 0.042 | 0.253 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".