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Ambulatory carriage device (SMHeartCard) for pre-hospital therapy of myocardial infarction: A case report and case series

2022· article· en· W4229445310 on OpenAlexaff
Donald H. Paterson, John R. Mackey, Peter Stewart, Beng Peng, Neal M. Davies

Bibliographic record

VenueJournal of Medical Case Reports and Case Series · 2022
Typearticle
Languageen
FieldMedicine
TopicHealthcare Technology and Patient Monitoring
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCarriageAmbulatoryMedicineMyocardial infarctionCardiologySeries (stratigraphy)Intensive care medicineInternal medicinePathology

Abstract

fetched live from OpenAlex

SMHeartCard is an on-person carriage device for ASA and Nitroglycerin for pre-hospital use at the onset of symptoms of myocardial infarction, designed to improve access and reduce the time from symptom onset to therapy.We present a case of an 86-year-old male with risk factors for coronary atherosclerosis, who used SMHeartCard at the onset of symptoms.His clinical course was excellent, with the maintenance of normal ejection fraction and absence of heart failure symptoms, and he remains well more than two years later.A case series of sixteen additional SMHeartCard users, compiled from post-marketing feedback to the manufacturer, suggests generally favorable outcomes in this population.In aggregate, these observations, and the well-defined advantages of rapid medical therapy for acute coronary events, suggest SMHeartCard can be used effectively by persons at risk of myocardial infarction.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.003
Science and technology studies0.0040.003
Scholarly communication0.0030.003
Open science0.0020.003
Research integrity0.0090.005
Insufficient payload (model declined to judge)0.0040.002

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 head0.024
GPT teacher head0.331
Teacher spread0.307 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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

Citations0
Published2022
Admission routes1
Has abstractyes

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