91 SYSTOLIC TIME INTERVALS: A PSYCHOSOMATIC LINK IN NEUROVASCULAR DISORDERS.
Bibliographic record
Abstract
Objective: To identify and compare the operating characteristics of existing prehospital stroke scales to predict true strokes in the hospital. Methods: We searched MEDLINE, EMBASE, and CINAHL databases for articles that evaluated the performance of prehospital stroke scales. Quality of the included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies–2 tool. We abstracted the operating characteristics of published prehospital stroke scales and compared them statistically and graphically. Results: We retrieved 254 articles from MEDLINE, 66 articles from EMBASE, and 32 articles from CINAHL Plus database. Of these, 8 studies met all our inclusion criteria, and they studied Cincinnati Pre-Hospital Stroke Scale (CPSS), Los Angeles Pre-Hospital Stroke Screen (LAPSS), Melbourne Ambulance Stroke Screen (MASS), Medic Prehospital Assessment for Code Stroke (Med PACS), Ontario Prehospital Stroke Screening Tool (OPSS), Recognition of Stroke in the Emergency Room (ROSIER), and Face Arm Speech Test (FAST). Although the point estimates for LAPSS accuracy were better than CPSS, they had overlapping confidence intervals on the symmetric summary receiver operating characteristic curve. OPSS performed similar to LAPSS whereas MASS, Med PACS, ROSIER, and FAST had less favorable overall operating characteristics. Conclusions: Prehospital stroke scales varied in their accuracy and missed up to 30% of acute strokes in the field. Inconsistencies in performance may be due to sample size disparity, variability in stroke scale training, and divergent provider educational standards. Although LAPSS performed more consistently, visual comparison of graphical analysis revealed that LAPSS and CPSS had similar diagnostic capabilities.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".