Role of Proxy Respondents in International Stroke Research: Experience of the INTERSTROKE Study
Bibliographic record
Abstract
INTRODUCTION: Measuring patient-reported information in stroke research is challenging. To overcome this, use of proxy respondents is often a necessary strategy. In this study, we report on use and effect of proxy respondents on patient case-mix in a large international epidemiologic stroke study (INTERSTROKE). METHODS: This was a cross-sectional study of 13,458 cases of acute first stroke in 32 countries. A standardized study questionnaire recording behavioural cardiovascular risk factors was administered to the patient, and if unable to communicate adequately, a valid proxy, or both. We used logistic regression to evaluate the association of age, sex, education, occupation, stroke severity, and region with need for proxy respondent, and report odds ratio (OR) with 95% confidence interval (CI). RESULTS: Among 13,458 participants with acute stroke, questionnaires were completed by patients alone in 41.4% (n = 5,573), combination of patient and proxy together in 21.7% (n = 2,918), and proxy alone in 36.9% (n = 4,967). Use of proxy alone was greater in participants with severe stroke (4.7% with modified-Rankin score of 0 vs. 80.5% in those with score 5; OR 187.13; 95% CI: 119.61-308.22), older persons (43.8% of those aged 80 years and over vs. 33.2% of those aged less than 40 years; age per decade OR 1.09; 95% CI: 1.06-1.12), women (40.7% vs. 34.3% of men; OR 1.32 95% CI: 1.22-1.43), and those less educated (58.9% of those never educated vs. 25.7% of those who attended third level education; OR 7.84; 95% CI: 6.78-9.08). CONCLUSION: Use of proxy respondents enhances the generalizability of international research studies of stroke, by increasing representation of women, patients with severe stroke, older age, and lower education.
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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 | 0.086 | 0.116 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".