Magnitude of Rater Differences in Assessment Scales for Schizophrenia
Bibliographic record
Abstract
The magnitude of rater differences, instead of interrater reliability, in the assessment scales of schizophrenia has rarely been investigated and was therefore addressed in this study. Thirty-six patients with schizophrenia were independently assessed by 4 expert physicians, using clinical rating scales including the Positive and Negative Syndrome Scale (PANSS). The scores obtained by the physician in charge (PIC), who had a long close contact with the patients, served as the referent answer for the purpose of this study. The scores rated by the other 3 non-PIC psychiatrists, who had a first formal examination with them, were evaluated for percentage deviance from the referent answer. The results showed that the PIC raters endorsed the numerically highest score in 20 (56%) of the 36 patients, whereas they rated the lowest in only 2 (6%) in the PANSS total score. The non-PIC assessors on the average underrated the PANSS total score by 10%, and such a tendency of underestimating the severity was noted across other clinical scales. Furthermore, the PANSS total score by one of the non-PIC physicians was deviant from the referent answer by at least 20% in 15 (42%) of 36 instances. Importantly, this magnitude of deviance was noted in the context of an intraclass correlation coefficient of 0.92. This unique investigation disclosed clinically pertinent differences among raters, even under an excellent interrater reliability. The magnitude of differences described herein seems to be an underestimation, and the baseline scores by the independent new raters might need to be corrected for those by the PICs.
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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 | 0.207 | 0.349 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".