Evaluating a complex intervention with a single outcome may not be a good idea: an example from a randomised trial of stroke case management
Bibliographic record
Abstract
OBJECTIVES: to estimate the extent to which a case-management intervention for persons newly discharged into the community following an acute stroke effected a change in stroke outcome in comparison with usual care. DESIGN: a re-analysis of stratified, balanced, randomised clinical trial. SETTING: five university-affiliated acute-care hospitals in Montreal, Quebec, Canada. PARTICIPANTS: a total of 190 persons (mean age 70 years) returning home directly from the acute-care hospital following a first or recurrent stroke with a need for health-care supervision post-discharge because of low function, co-morbidity or isolation. INTERVENTION: for 6 weeks following discharge a nurse case manager delivered, depending on need, over 50 different nursing interventions (range 2-15 per person), which targeted physical, emotional and psychological impairments, role participation restrictions and health perception. MEASUREMENTS: seven of the SF-36 subscales were used to measure the targeted constructs, at the post-intervention and 6 month evaluations. Seven binary response variables were created with a change of 10 points the criterion for individual response. Generalised estimating equations, equivalent to a logistic regression for multiple outcomes, were used. RESULTS: the odds of responding to one or more outcomes was 41% greater in the intervention group than in the control group [odds ratio (OR): 1.41; 95% confidence interval (CI): 1.11-1.79]. CONCLUSION: an analysis considering the complexity of the intervention and outcomes targeted indicated effectiveness of the nurse case-management post-stroke, whereas the traditional one outcome analysis did not.
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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 | 0.000 | 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 | 0.000 |
| 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".