Patterns, Predictors, Variations, and Temporal Trends in Emergency Medical Service Hospital Prenotification for Acute Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND#ENTITYSTARTX02014;: Emergency medical services (EMS) hospital prenotification of an incoming stroke patient is guideline recommended as a means of increasing the timeliness with which stroke patients are evaluated and treated. Still, data are limited with regard to national use of, variations in, and temporal trends in EMS prenotification and associated predictors of its use. METHODS AND RESULTS#ENTITYSTARTX02014;: We examined 371 988 patients with acute ischemic stroke who were transported by EMS and enrolled in 1585 hospitals participating in Get With The Guidelines-Stroke from April 1, 2003, through March 31, 2011. Prenotification occurred in 249 197 EMS-transported patients (67.0%) and varied widely by hospital (range, 0% to 100%). Substantial variations by geographic regions and by state, ranging from 19.7% in Washington, DC, to 93.4% in Montana, also were noted. Patient factors associated with lower use of prenotification included older age, diabetes mellitus, and peripheral vascular disease. Prenotification was less likely for black patients than for white patients (adjusted odds ratio 0.94, 95% confidence interval 0.92-0.97, P<0.0001). Hospital factors associated with greater EMS prenotification use were absence of academic affiliation, higher annual volume of tissue plasminogen activator administration, and geographic location outside the Northeast. Temporal improvements in prenotification rates showed a modest general increase, from 58.0% in 2003 to 67.3% in 2011 (P temporal trend <0.0001). CONCLUSIONS#ENTITYSTARTX02014;: EMS hospital prenotification is guideline recommended, yet among patients transported to Get With The Guidelines-Stroke hospitals it is not provided for 1 in 3 EMS-arriving patients with acute ischemic stroke and varies substantially by hospital, state, and region. These results support the need for enhanced implementation of stroke systems of care. (J Am Heart Assoc. 2012;1:e002345 doi: 10.1161/JAHA.112.002345.).
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".