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Record W2272099437

Adverse Drug Reactions in the Emergency Department Population in Ontario: Analysis of National Ambulatory Care Reporting System and Discharge Abstract Database 2003-2007

2009· dissertation· en· W2272099437 on OpenAlexaboutno aff
Chen Wu

Bibliographic record

VenueTSpace (University of Toronto) · 2009
Typedissertation
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineEmergency departmentIncidence (geometry)Emergency medicinePharmacyAmbulatoryAmbulatory careCohortHealth carePopulationDrug reactionComorbidityRetrospective cohort studyPediatricsMedical emergencyFamily medicineDrugEnvironmental healthInternal medicinePsychiatry
DOInot available

Abstract

fetched live from OpenAlex

ADR is an important public health problem which reduces quality of care patients receive and increases cost to healthcare system. Little is known about the incidence and economic burden of ADR-related ED visits and subsequent hospitalizations in Canada. This study estimated the incidence and cost of ADR-related ED visits and subsequent hospitalizations for patients (>65 years) in Ontario, and explored patient, drug and system factors associated with severe ADRs. In a population-based retrospective cohort of Ontario older adults, 7222 (0.75%) of all ED visits were ADR-related, and among these patients 21.56% were hospitalized in 2007; In 2007, the total measured cost of ADR-related visits and subsequent hospitalizations amounted to $13.6 million with the cost being $333.47 and $7528.64 per person for ED visits and subsequent hospitalizations, respectively. Severe ADRs were associated with sex, age, comorbidity, multiple drugs, multiple pharmacies, newly prescribed drugs, recent ED visit, recent hospitalization and LTC residents.

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 categoriesInsufficient 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 score0.216
Threshold uncertainty score1.000

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)0.0010.000

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 head0.055
GPT teacher head0.362
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 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

Citations14
Published2009
Admission routes1
Has abstractyes

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