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Record W1966993240 · doi:10.1186/cc13674

Implementation of the PulsePoint smartphone application for crowd-sourcing bystander resuscitation

2014· article· en· W1966993240 on OpenAlexaff
SC Brooks, Heather Worthington, Toula E Gonedalles, Bentley J. Bobrow, LJ Morrison

Bibliographic record

VenueCritical Care · 2014
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsUniversity of TorontoSt. Michael's HospitalQueen's University
Fundersnot available
KeywordsBystander effectMedicineCrowd sourcingMedical emergencyEmergency medicineData science

Abstract

fetched live from OpenAlex

Only a minority of patients suffering out-of-hospital cardiac arrest receive any bystander cardiopulmonary resuscitation (CPR). Bystander CPR is associated with improved odds for survival. The PulsePoint smartphone application alerts users in the vicinity of a cardiac arrest to facilitate immediate citizen bystander resuscitation. Addressing implementation barriers may provide an opportunity to increase effectiveness of the application. PulsePoint is currently active in over 400 communities in the United States. Our objective was to identify modifiable barriers to optimal implementation of the PulsePoint smartphone application.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.589
Threshold uncertainty score0.205

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.342
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

Citations13
Published2014
Admission routes1
Has abstractyes

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