MétaCan
Menu
Back to cohort

Vaccination of Emergency Department Patients at High Risk for Influenza

2000· article· en· W2015475506 on OpenAlexaffabout
Atul K. Kapur, Milton Tenenbein

Bibliographic record

VenueAcademic Emergency Medicine · 2000
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMedicineVaccinationEmergency departmentEmergency medicineMedical emergencyPediatricsImmunology

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine the proportion of Canadian emergency department (ED) patients who are at risk for increased morbidity from influenza but were not vaccinated and to determine emergency physicians' (EPs') willingness to screen for and prescribe influenza vaccination. METHODS: The authors surveyed a convenience sample of patients presenting during a one-week period at each of four EDs in Winnipeg, Manitoba, Canada, after the end of the seasonal period for vaccination. They also surveyed all full-time EPs in Winnipeg. RESULTS: Fifty-three percent of emergency patients at risk for increased morbidity from influenza had not been vaccinated and 59.3% of them were willing to be vaccinated during an emergency visit. This represents 31.6% (+/-3.1%) of all high-risk patients and 15% of all emergency patients. High-risk patients who did not have a regular physician were less likely to have been vaccinated (OR 0.165, p = 0.018). Most EPs rarely or never offer influenza vaccination (30% and 57%, respectively). Seventy-six percent of them were willing to prescribe vaccination. CONCLUSION: Many ED patients are at risk for increased morbidity from influenza and have not been vaccinated. The majority of them are willing to be vaccinated during an emergency visit and the majority of EPs are willing to prescribe vaccination. Emergency department vaccination for influenza should be considered as a strategy to increase vaccination among high-risk groups.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0220.000

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 head0.085
GPT teacher head0.419
Teacher spread0.334 · 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.

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

Citations26
Published2000
Admission routes2
Has abstractyes

Explore more

Same venueAcademic Emergency MedicineSame topicInfluenza Virus Research StudiesFrench-language works237,207