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 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 | 0.055 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".