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Record W2019358687 · doi:10.1016/s1474-5151(10)60027-8

45 Oral Sex and Gender Differences in Critical Time-to-Treatment Intervals among ST-Elevation MI Patients

2010· article· en· W2019358687 on OpenAlexaff
Martha Mackay, M. Perry, Graham C. Wong

Bibliographic record

VenueEuropean Journal of Cardiovascular Nursing · 2010
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsVancouver General HospitalVancouver Coastal HealthSt. Paul's Hospital
Fundersnot available
KeywordsMedicineElevation (ballistics)Internal medicineDemographyGender disparity

Abstract

fetched live from OpenAlex

Early initiation of treatment is essential for favourable outcomes in acute ST-elevation myocardial infarction (STEMI). However, achieving this is linked to a cascade of events, including establishing an accurate diagnosis quickly, and prompt treatment-seeking by patients. Because of this interdependence research attention has focused on understanding where delays exist, what factors may be influencing this, and more recently, how to mitigate them. Many investigators have found that women delay treatment-seeking for symptoms of acute coronary syndromes (ACS) longer than men do. And, disappointingly, in spite of some success in improving time-to-treatment intervals once patients have entered the healthcare system, little gain has been reported in reducing patients' treatment-seeking delays. Methods: Using a registry of STEMI patients, we examined whether there are sex/gender differences in time-to-treatment sub-intervals: symptom onset to first medical contact, and first medical contact to first ECG. Since a programme for pre-hospital acquisition of 12-lead ECGs has recently been implemented in the region, the analysis was further divided into two time periods (before and after implementation of the programme), to ascertain if it affected either interval. Analysis of variance was used to explore these interactions.

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)0.000
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 score0.783
Threshold uncertainty score0.465

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.034
GPT teacher head0.287
Teacher spread0.253 · 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

Citations1
Published2010
Admission routes1
Has abstractyes

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