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Record W2133941927 · doi:10.1017/s0714980810000383

Is There an Age Pattern in the Treatment of AMI? Evidence from Ontario

2010· article· fr· W2133941927 on OpenAlexaffabout
Michel Grignon, Byron G. Spencer, Li Wang

Bibliographic record

VenueCanadian Journal on Aging / La Revue canadienne du vieillissement · 2010
Typearticle
Languagefr
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineMyocardial infarctionEmergency medicineAcute careDemographyMedical emergencyInternal medicineHealth care

Abstract

fetched live from OpenAlex

In this article we analyse the rates at which those admitted to hospital with acute myocardial infarction (AMI) receive aggressive treatment, assess how those rates have changed over time, and ask whether there is evidence of age discrepancies. Estimates made on the basis of data from an administrative database that includes discharges from all acute care hospitals in Ontario for selected years, from 1995 to 2005, indicate that there are strong and persistent age patterns in the application of medical technology. Results showed that to be true even after controlling for the higher rates of co-morbidities among older patients and variations across hospitals in practice patterns.

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)0.003
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.980
Threshold uncertainty score0.143

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.006
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.048
GPT teacher head0.250
Teacher spread0.203 · 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 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

Citations1
Published2010
Admission routes2
Has abstractyes

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