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Record W3038256232 · doi:10.1377/hlthaff.2019.00842

EMS Access Constraints And Response Time Delays For Deprived Critically Ill Patients Near Paris, France

2020· article· en· W3038256232 on OpenAlexaff
Matthieu Heidet, Thierry Da Cunha, Elise Brami, Éric Mermet, M. Dru, Béatrice Simonnard, Éric Lecarpentier, C. Chollet-Xémard, Corinne Bergeron, Mohamed Khalid, Brian Grunau, J. Marty, Étienne Audureau

Bibliographic record

VenueHealth Affairs · 2020
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineLogistic regressionEmergency medical servicesSocioeconomic statusMultivariate analysisMedical emergencyEmergency medicineCohortMultivariate statisticsMultilevel modelCritically illIntensive care medicinePopulationEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

Increased emergency medical services (EMS) response times and areas of low socioeconomic status are both associated with poorer outcomes for several time-sensitive medical conditions attended to by medical personnel before a patient is hospitalized. We evaluated the association between EMS response times, area deprivation level, and on-scene access constraints encountered by EMS in a large urban area in France. We conducted a multicenter prospective cohort study of EMS dispatches occurring in the forty-seven cities in a region southeast of Paris. We fit multilevel mixed-effects linear regression models for multivariate assessment of the predictors of EMS response times and then used multivariate logistic regression on outcomes among a subgroup of patients presenting with out-of-hospital cardiac arrest. We found evidence that access constraints were more frequently encountered by EMS in the most deprived areas compared to less deprived ones, and were associated with increased EMS response times until patient contact and with poorer outcomes from cardiac arrest. Strategies to anticipate and overcome access constraints should be implemented to improve outcomes for emergent conditions attended to by prehospital medical teams.

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)0.002
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 score0.349
Threshold uncertainty score0.422

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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 head0.017
GPT teacher head0.315
Teacher spread0.298 · 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
Published2020
Admission routes1
Has abstractyes

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