A measurable treatment plan: Using the Children’s Global Assessment and the Problem Severity scales as outcomes of clinical treatment
Bibliographic record
Abstract
Objective: To investigate the psychometric properties of the Children’s Global Assessment Scale (CGAS) and the strength/concern (Problem Severity [PS]/Symptom) scale, collectively known as the measurable treatment plan (MTP).Methods: We draw on a sample of consecutive patients (n = 25,563) enrolled for care between 2002 and 2016 in the Child & Adolescent Addictions and Mental Health Psychiatry Program of the Alberta Health Services, Calgary Health Zone. CGAS reliability was estimated using the Pearson Product-Moment correlation for repeated measures between referral and admission. For the internal consistency of paired referral and admission CGAS scores, α = 0.82. We estimated the predictive validity of the CGAS and the strength/concern scale using analyses of variance with the demographic variables age and sex, and additionally the system variables service level, treatment completion and provisional diagnosis as covariates of analysis in a final reduced model or as independent variables where warranted.Results: We discovered that there is a high level of agreement between paired referral and admission CGAS scores We also discerned functional improvement and symptom reduction on discharge which was attributable to the effect of treatment alone. Importantly, patients who were categorized at the urgent/emergent service level of care at admission, exhibited more severe provisional diagnoses, and/or discontinued treatment attained lower function and PS ratings at discharge.Conclusions: Overall, the current study supports the empirical applicability of using the MTP to clinically profile on admission those at risk of poor treatment outcomes and to undertake necessary modifications to the treatment process.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".