MétaCan
Menu
Back to cohort
Record W2003931499 · doi:10.3109/02770903.2013.873453

Use of emergency departments and primary care visits for asthma related conditions in the 3 years following an asthma education program

2013· article· en· W2003931499 on OpenAlexafffundabout
Katherine Gaudreau, Henrik Stryhn, Carolyn Sanford, Connie Cheverie, Janette Conklin, Judy Hansen, Mitchell Zelman, Carol McClure

Bibliographic record

VenueJournal of Asthma · 2013
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsHealth PEIUniversity of Prince Edward Island
FundersPublic Health Agency of Canada
KeywordsMedicineAsthmaReferralEmergency departmentPediatricsPrimary careEmergency medicineFamily medicineInternal medicineNursing

Abstract

fetched live from OpenAlex

BACKGROUND: This study examines changes in Primary Care Visits (PCVs) and Emergency Department Visits (EDVs) among 1918 patients with asthma who attended either two visits, one visit or were no-show referrals at the Dr. Patrick Gill Asthma Education Center (AEC) in Charlottetown Prince Edward Island (PEI) between January 1, 2003 and March 31, 2008 compared to 2799 controls selected from a list of PEI asthma patients developed for the Canadian Chronic Disease Surveillance System (CCDSS). METHODS: Hurdle regression was used to model counts of PCVs and negative binomial models were used to model counts of EDVs at 12 months prior to AEC contact and 0-1, >1 to 2 and >2 to 3 years after AEC contact. The PEI Research Board approved the project. RESULTS: No-show referrals had a significant increase in pediatric EDVs and PCVs in the first year after referral. The higher rates of PCVs and EDVs prior to contact with the AEC in patients referred to the AEC were reduced after contact with the AEC, although they remained significantly higher than the CCDSS controls. CONCLUSIONS: Compared to patients who attended the AEC, referred patients who did not attend the AEC did not achieve similar reductions in pediatric EDVs and PCVs in the first year after referral.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.621
Threshold uncertainty score0.350

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 communication0.0000.001
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 head0.019
GPT teacher head0.324
Teacher spread0.305 · 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

Citations9
Published2013
Admission routes3
Has abstractyes

Explore more

Same venueJournal of AsthmaSame topicAsthma and respiratory diseasesFrench-language works237,207