Quality of Ischemic Stroke Care in Emerging Countries
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Limited information is available on stroke management in developing countries. An accurate monitoring of quality of stroke care will become crucial, particularly with the emerging paradigm of pay-for-performance. Our aim was to explore the feasibility of measuring standardized indicators of quality of ischemic stroke care in acute care facilities in Argentina. METHODS: ReNACer is a prospective, multicenter, countrywide, stroke registry comprising 74 academic and nonacademic institutions in Argentina. The registry includes patient-level information on demography, clinical characteristics, diagnostic procedures, treatment, and the selected key performance indicators of quality of ischemic stroke care (access to thrombolysis or aspirin use in the acute setting, admission to designated stroke units, length of stay, risk-adjusted in-hospital pneumonia, risk-adjusted in-hospital mortality, discharge on antithrombotics, and antihypertensive agents). RESULTS: We included 1991 patients with ischemic stroke from 74 institutions in Argentina between November 2004 and October 2006. Seventy-nine per cent of the patients were prescribed antithrombotic therapy within 48 hours of admission, but only 1% received thrombolytics. No more than 5.7% were admitted to stroke units. In-hospital pneumonia was diagnosed in 14.3% of the patients and was higher in nonacademic facilities (16.4% versus 11.4%, P<0.02). The overall adjusted in-hospital mortality was 9.1%, also higher in nonacademic hospitals (10.6% versus 7.1%, P<0.008). At discharge, antithrombotics were prescribed in 90.2% and antihypertensive agents in 63.6% of the patients. CONCLUSIONS: In ReNACer, there was a limited access to stroke units and thrombolytics, and a relatively high incidence of in-hospital pneumonia. Differences in stroke care were observed between academic and nonacademic institutions. There is an urgent need to develop national stroke programs in Argentina.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".