Predictors of symptomatic and wellbeing remission in real-world samples of patients living with schizophrenia treated with aripiprazole once-monthly by means of constrained confidence partitioning
Bibliographic record
Abstract
Introduction An important goal in the treatment of patients living with schizophrenia is to achieve remission of schizophrenia symptoms and patient well-being. The possibility to predict which patients will achieve remission may inform treatment decisions. We derived different models for this on the basis of data from a German real-world study, part of which were tested for their predictive power by independent data obtained from a similar Canadian study. Methods Here, we used a sample of patients living with schizophrenia who participated in a 6-month non-interventional study of aripiprazole once-monthly. Data of patients with complete datasets ( n = 194) were used to predict remission of symptoms (cross-sectional Andreasen criteria as measured by the Brief Psychiatric Rating Scale, BPRS) and well-being (as measured by the WHO-5 well-being index) at 6 months using logistic regression as well as the constrained confidence partitioning (c 2 p) method. Results Logistic regression yielded a model with the variables remission stats at baseline, Global Assessment of Functioning (GAF) score at baseline and age. Quality indicators suggested acceptable model quality, and we were able to validate the model using external data from the Canadian study. Models for remission of well-being and combined remission of symptoms and well-being were of poor quality. Modeling using c 2 p yielded defined groups of patients with differential likelihoods of achieving remission of symptoms, well-being, or both. In general, patients with lower scores in core symptoms, higher GAF scores, younger age, and higher WHO-5 scores showed increased likelihoods of achieving remission. The c 2 p model of symptomatic remission was also validated using external data from the Canadian study, and we demonstrated the possibility to generate and test hypotheses based on the model. Conclusion Despite only using a comparatively small sample, both logistic regression and c 2 p can produce reliable results with mathematically desirable properties. Patients with less severe core symptoms, better functioning, younger age, and better well-being may achieve symptomatic remission and remission of well-being more easily than other patients. The latter may need additional interventions in order to achieve remission.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".