P02-130 - Measuring Complexity in the Age of Information and Accountability: a New Approach to Monitoring Treatment Responses and Client Tracking (TRACT)
Bibliographic record
Abstract
Objectives Measuring clinical and functional outcomes in mental health is becoming an increasingly complex and costly endeavour. It is with considerable regularity that tens and hundreds of millions of dollars are spent to develop electronic health records, yet today functional integrated products that can operate across domains of health have yet to be identified. The purpose of this paper is to describe ways and means of measuring functional and clinical outcomes across complicated case mixes and treatment domains in hospital and community treatment settings. Methods Data from several electronic records and data sources (n = 100,000) are used to illustrate the burden of measurement, data quality and related issues that practitioners face when collecting, analyzing and interpreting data in the age of information and accountability in health care. A practical, flexible, modular, and dynamic tool for measuring functional and clinical outcomes across treatment and education settings is described (TRACT: treatment response application for client tracking). Results Relatively simple, modular approaches to clinical and functional outcome measurement that are integrated into medical practice have the lowest burden and highest yield in terms of demonstrating evidence-based practice, treatment effectiveness, and system level accountability. Conclusions Simple modular approaches to measuring complex phenomena, such as the effect of multiple treatment interventions in complex environments and against backgrounds of comorbid disorders, are likely to have high quality yields in terms of identifying promising and evidence-based practices through use of the highest standards available to an examination of practice in the field.
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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 | 0.027 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".