Smartphone-Enabled Point-of-Care Testing for Prehospital Stroke Diagnosis
Bibliographic record
Abstract
Objectives The objective of this study was to evaluate the feasibility of point-of-care testing (POCT) devices for N-terminal pro-B-type natriuretic peptide (NT-proBNP) measurement in prehospital settings, with the aim of improving the speed and accuracy of stroke diagnosis, thereby facilitating quicker and more effective patient care.Methods Prehospital blood samples were collected from suspected stroke patients, and NT-proBNP levels were measured using a POCT device in ambulances and hospitals. Results from the NT-proBNP POCT and smartphone images were analyzed. Plasma samples underwent Elecsys proBNP II immunoassay after storage at −80ºC.Results A total of 121 suspected stroke patients were included in the study. The correlation between POCT measured by the POCT and immunoassay for NT-proBNP was strong (R = 0.926). Smartphone images also strongly correlated with POCT values at 10 min (R²=0.9716) and 15 min (R²=0.9405). Stability analysis of samples showed consistent NT-proBNP results and a high correlation (R = 0.907) was observed between plasma and whole blood samples for NT-proBNP POCT.Conclusions This study highlights the potential of NT-proBNP POCT devices in ambulances to expedite stroke diagnosis and management within 10 min. Smartphone integration further enhances efficiency, adding advancement in prehospital stroke management.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".