P01-185 - Predicting Risk for Poor Outcomes in Children's Mental Health
Bibliographic record
Abstract
Objectives Using baseline and outcome data collected in the Calgary Region Children and Adolescent Mental Health Program (CAMHP) over the last six years, a profile was developed for those who are at risk for poor treatment outcomes. Methods Based on the data collected in CAMHP, 6229 completed measurable treatment plans (MTP) were analyzed for consistency (by year) and theoretical meaningfulness (by clinical level). A table was developed to describe by quartiles those who improved and those who got worse in terms of problem severity and function. A multi-variable linear regression model was developed for function with linked data that predicted the profile of those at risk for poor treatment outcomes. Results MTP scores reflecting problem severity and function were consistent over time. Further, MTP scores were theoretically meaningful (e.g., inpatients were more severe on admission than those receiving community or day hospital-based treatment). In total, 659 MTPs indicated no improvement for a negative state and an additional 830 changed negatively in problem severity and Children's Global Assessment Scale (C-GAS) scores. The multivariable model based on admission data provided a risk profile for this group that accounted for 52% of the variance for discharge function. Conclusions The baseline and outcome data gathered from the Regional Access and Intake System (RAIS) serving CAMHP may be used effectively to predict clinical outcomes in ways that can draw attention to those at risk for poor outcomes.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".