Predicting individual change during the course of treatment
Bibliographic record
Abstract
OBJECTIVE: An empirically derived prediction model was developed in a private practice setting to monitor on-track and off-track weekly treatment progress in an intensive outpatient program (IOP). METHOD: The predictive equation was derived as a function of the baseline measure and time. The formulae for the predictive equations were derived from two groups of psychiatric patients (N = 400 each) in an IOP diagnosed with major depression. Each equation was cross-validated between these two psychiatric IOP samples and a dual diagnosis sample (N = 198) using κ, the reliable change index (RCI), receiver operating characteristic curves, and Youden's J. RESULTS: Using varying RCI classifications, approximately 66-75% of both samples reliably improved, 23-24% were indeterminant, and only 1-3% deteriorated. Of patients identified as off-track, which included patients classified as indeterminant and deteriorated, 83% were correctly identified. Of those identified as on-track, 85% were correctly classified. Those identified as on-track (85%) are highly likely to respond to treatment as expected. CONCLUSIONS: The overall efficiency index (hit rate) for the correct classification of all patients was 85%. Implications for using this predictive model as a clinical support decision tool with relatively homogeneous populations in other practice settings are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".