Prediction of quality of life in schizophrenia using machine learning models on data from Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) schizophrenia trial
Bibliographic record
Abstract
While research focus remains mainly on psychotic symptoms, it is questionable whether we are placing enough emphasis on improving the quality of life (QoL) of schizophrenia patients. To date, the predictive power of QoL remained limited. Therefore, this study aimed to accurately predict the QoL within schizophrenia using supervised learning methods. The authors report findings from participants of a large randomized, double-blind clinical trial for schizophrenia treatment. Potential predictors of QoL included all available and non-redundant variables from the dataset. By optimizing parameters, three linear LASSO regressions were calculated (N = 697, 692, and 786), including 44, 47, and 41 variables, with adjusted R-squares ranging from 0.31 to 0.36. Best predictors included social and emotion-related symptoms, neurocognition (processing speed), education, female gender, treatment attitudes, and mental, emotional, and physical health. These results demonstrate that machine learning is an excellent predictive tool to process clinical data. It appears that the patient's perception of their treatment has an important impact on patients' QoL and that interventions should consider this aspect.Trial registration: ClinicalTrials.gov Identifier: NCT00014001.
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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 | 0.016 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".