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Record W2749913932 · doi:10.4103/jnrp.jnrp_2_17

Prehospital Management of Acute Stroke in Rural versus Urban Responders

2017· article· en· W2749913932 on OpenAlexaffabout
Gregory Hansen, Simerpreet Bal, Kerri Schellenberg, Susan Alcock, Esseddeeg Ghrooda

Bibliographic record

VenueJournal of Neurosciences in Rural Practice · 2017
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsUniversity of ManitobaHealth Sciences CentreUniversity of CalgaryUniversity of SaskatchewanRoyal University Hospital
Fundersnot available
KeywordsMedicineEmergency medical servicesStroke (engine)Emergency medicineRural areaAcute strokeMedical emergencyGuidelineEmergency departmentNursing

Abstract

fetched live from OpenAlex

OBJECTIVE: Stroke guideline compliance of rural Canadian prehospital emergency medical services (EMS) care in acute stroke is unknown. In this quality assurance study, we sought to compare rural and urban care by prehospital EMS evaluation/management indicators from patients assessed at an urban Canadian stroke center. MATERIALS AND METHODS: One hundred adult patients were randomly selected from the stroke registry. Patients were transported through Rural EMS bypass protocols or urban EMS protocols (both bypass and direct) to our stroke center between January and December 2013. Patients were excluded if they were first evaluated at any other health center. Prehospital care was assessed using ten indicators for EMS evaluation/management, as recommended by acute stroke guidelines. RESULTS: = 0.002). Unlike urban EMS, rural EMS did not routinely document scene time. CONCLUSION: Rural EMS responders' compliance to prehospital stroke evaluation/management was similar to urban EMS responders. Growth areas for both groups may be with prehospital stroke diagnostic tool utilization, whereas rural EMS responders may also improve with scene time documentation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.383
Threshold uncertainty score0.536

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.028
GPT teacher head0.357
Teacher spread0.329 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2017
Admission routes2
Has abstractyes

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