The importance of clinical normative data for conceptualizing neuropsychological deficits in people with schizophrenia spectrum disorders
Bibliographic record
Abstract
OBJECTIVE: To create clinical normative data tables for Norwegian patients with schizophrenia spectrum disorders, to examine whether clinical normative data from Norway differs from similar normative data from Canada and the U.S., and to illustrate the usefulness of such data. METHOD: A nationally representative sample of 335 patients from psychiatric hospitals in Bergen, Norway was included. Inclusion criteria were 18-39 years of age, Norwegian as first language, and symptoms of schizophrenia, psychosis, or hallucinations. Comorbid substance abuse was recorded in 134 (40.0%). All completed the Norwegian version of the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). RESULTS: The average scores of patients with schizophrenia spectrum disorders were approximately one to two standard deviations below the mean for healthy adults. There were no significant differences in scores between patients with or without comorbid substance abuse. Men had higher scores than women. Clinical normative reference value look-up tables were created. CONCLUSIONS: Clinical normative values were very similar to values from Canada and the U.S. Clinical normative data, as a supplement to standard healthy normative data, can be used to describe patients' cognitive performance in terms of expectation for their peer group which can be useful for multidisciplinary treatment planning.
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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 | 0.021 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".