Redefining outcome measures in schizophrenia: integrating social and clinical parameters
Bibliographic record
Abstract
PURPOSE OF REVIEW: Schizophrenia is a complex neurobehavioral disorder for which there are many promising new treatments. There is, however, a discrepancy in outcome measure reports when they are obtained from patients, relatives, caregivers, or professionals, making it difficult to determine the level of recovery. This lack of agreement may result from limitations of the measurement tools themselves, which are not comprehensive and may be measuring different aspects of outcome. Alternatively, it could be that the conceptual understanding of outcome and recovery require development. RECENT FINDINGS: For various reasons, patients assessed as 'recovered' remain excluded from mainstream society. We are of the opinion that present outcome measures do not capture real-life situations. We propose that the concept of recovery be carefully defined and the gold standard of outcome should incorporate social and clinical parameters. We attempt to redefine recovery. Patients who have shown clinical improvement do not necessarily do well in everyday situations even though there is obvious clinical improvement. Therefore, it has been repeatedly argued that a consensus of recovery should be determined and that routine clinical practice should then adapt to the agreed criteria. SUMMARY: We argue that the outcome measures should be multidimensional and consist of at least two parameters: clinical remission and social outcome.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".