A systematic review of stroke recognition instruments in hospital and prehospital settings
Bibliographic record
Abstract
BACKGROUND: We undertook a systematic review of all published stroke identification instruments to describe their performance characteristics when used prospectively in any clinical setting. METHODS: A search strategy was applied to Medline and Embase for material published prior to 10 August 2015. Two authors independently screened titles, and abstracts as necessary. Data including clinical setting, reported sensitivity, specificity, positive predictive value, negative predictive value were extracted independently by two reviewers. RESULTS: 5622 references were screened by title and or abstract. 18 papers and 3 conference abstracts were included after full text review. 7 instruments were identified; Face Arm Speech Test (FAST), Recognition of Stroke in the Emergency Room (ROSIER), Los Angeles Prehospital Stroke Screen (LAPSS), Melbourne Ambulance Stroke Scale (MASS), Ontario Prehospital Stroke Screening tool (OPSS), Medic Prehospital Assessment for Code Stroke (MedPACS) and Cincinnati Prehospital Stroke Scale (CPSS). Cohorts varied between 50 and 1225 individuals, with 17.5% to 92% subsequently receiving a stroke diagnosis. Sensitivity and specificity for the same instrument varied across clinical settings. Studies varied in terms of quality, scoring 13-31/36 points using modified Standards for the Reporting of Diagnostic accuracy studies checklist. There was considerable variation in the detail reported about patient demographics, characteristics of false-negative patients and service context. Prevalence of instrument detectable stroke varied between cohorts and over time. CPSS and the similar FAST test generally report the highest level of sensitivity, with more complex instruments such as LAPSS reporting higher specificity at the cost of lower detection rates. CONCLUSIONS: Available data do not allow a strong recommendation to be made about the superiority of a stroke recognition instrument. Choice of instrument depends on intended purpose, and the consequences of a false-negative or false-positive result.
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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 | 0.022 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.009 |
| Bibliometrics | 0.022 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".