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Effects of Trauma Cases on the Care of Patients Who Have Chest Pain in an Emergency Department

2000· article· en· W2076275005 on OpenAlexaff
Farid Boutros, Donald A. Redelmeier

Bibliographic record

VenueThe Journal of Trauma: Injury, Infection, and Critical Care · 2000
Typearticle
Languageen
FieldMedicine
TopicTrauma and Emergency Care Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEmergency departmentMedicineChest painEmergency medicineInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Trauma victims sometimes take priority over other patients because their injuries require immediate treatment. We examined whether such demands might compromise the care of patients with acute chest pain in an emergency department. METHODS: Case patients were patients with chest pain who arrived immediately after a major trauma victim. Control patients were patients with chest pain who arrived on a preceding day when no trauma patient was in the emergency department. RESULTS: Case and control patients were similar in mean age (60 vs. 60 years, p = not significant), percentage male (47 vs. 53%, p = not significant) and percentage ultimately diagnosed as cardiac (29 vs. 33%, p = not significant). Case patients spent an average of 81 minutes longer in the emergency department (297 vs. 216 minutes, p = 0.009). Similar delays were observed in the subgroup of patients ultimately diagnosed as cardiac (309 vs. 217 minutes, p = 0.029). Case patients had generally worse scores on the American College of Emergency Physicians Quality Assurance Index (75.6 vs. 84.4, p = 0.027), particularly those ultimately diagnosed as cardiac (60.3 vs. 85.1, p = 0.002). The common failures were failure to administer aspirin, undertreatment of ongoing pain, and failure to provide instructions regarding treatment and need to return. CONCLUSION: Trauma victims can decrease the timeliness and quality of care for other patients who have potentially life-threatening conditions in an emergency department.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.330
Threshold uncertainty score0.469

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 head0.017
GPT teacher head0.308
Teacher spread0.292 · 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

Citations27
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Trauma: Injury, Infection, and Critical CareSame topicTrauma and Emergency Care StudiesFrench-language works237,207