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Record W2793603994 · doi:10.1377/hlthaff.2017.1150

Accumulation Of Chronic Conditions At The Time Of Death Increased In Ontario From 1994 To 2013

2018· article· en· W2793603994 on OpenAlexafffundabout
Laura C. Rosella, Kathy Kornas, Anjie Huang, Catherine Bornbaum, David Henry, Walter P. Wodchis

Bibliographic record

VenueHealth Affairs · 2018
Typearticle
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsPublic Health OntarioInstitute for Clinical Evaluative Sciences
FundersCanadian Institutes of Health Research
KeywordsSocioeconomic statusMedicineMultimorbidityDemographyFalling (accident)Diabetes mellitusCause of deathGerontologyStroke (engine)Chronic diseaseEnvironmental healthInternal medicineDiseasePopulation

Abstract

fetched live from OpenAlex

With falling mortality rates for several diseases, patients are living longer with complex multimorbidities. We explored the burden of multimorbidity at the time of death, how it varies by socioeconomic status, and trends over time in Ontario, Canada. We calculated the proportions of decedents with varying degrees of multimorbidity and types of conditions at death, and we analyzed the trend from 1994 to 2013 in the number of conditions at the time of death. The prevalence of multimorbidity at death increased from 79.6 percent in 1994 to 95.3 percent in 2013. An upward trend in the number of conditions per person at death was observed for all chronic conditions except chronic coronary syndrome, congestive heart failure, and stroke. Chronic respiratory diseases and diabetes were disproportionately represented in low-income and deprived neighborhoods. The trend toward greater multimorbidity burden over time and the existence of steep socioeconomic gradients underscore the importance of integrated health care planning for preventing and managing multiple complex conditions.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.581
Threshold uncertainty score0.996

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)0.0050.000

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 head0.065
GPT teacher head0.369
Teacher spread0.304 · 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

Citations47
Published2018
Admission routes3
Has abstractyes

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