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Record W2779578298 · doi:10.1136/bmjopen-2017-018488

Senior high-cost healthcare users’ resource utilization and outcomes: a protocol of a retrospective matched cohort study in Canada

2017· article· en· W2779578298 on OpenAlexafffundabout
Sergei Muratov, Justin Lee, Anne Holbrook, J. Michael Paterson, Jason R. Guertin, Lawrence Mbuagbaw, Tara Gomes, Wayne Khuu, Priscila Pequeno, Andrew P. Costa, Jean‐Éric Tarride

Bibliographic record

VenueBMJ Open · 2017
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsSt. Michael's HospitalUniversité LavalHamilton Health SciencesMcMaster UniversityPrograms for Assessment of Technology in Health Research InstituteSt. Joseph’s Healthcare HamiltonInstitute for Clinical Evaluative SciencesImpact
FundersCanadian Institutes of Health ResearchOntario Ministry of Health and Long-Term Care
KeywordsMedicineRetrospective cohort studyHealth careProtocol (science)CohortCohort studyEpidemiologyHealth services researchFamily medicineResource (disambiguation)Health economicsPublic healthGerontologyAlternative medicineNursingSurgeryInternal medicinePathology

Abstract

fetched live from OpenAlex

INTRODUCTION: Senior high-cost users (HCUs) are estimated to represent 60% of all HCUs in Ontario, Canada's most populous province. To improve our understanding of individual and health system characteristics related to senior HCUs, we will examine incident senior HCUs to determine their incremental healthcare utilisation and costs, characteristics of index hospitalisation episodes, mortality and their regional variation across Ontario. METHODS AND ANALYSIS: A retrospective, population-based cohort study using administrative healthcare records will be used. Incident senior HCUs will be defined as Ontarians aged ≥66 years who were in the top 5% of healthcare cost users during fiscal year 2013 but not during fiscal year 2012. Each HCU will be matched to three non-HCUs by age, sex and health planning region. Incremental healthcare use and costs will be determined using the method of recycled predictions. We will apply multivariable logistic regression to determine patient and health service factors associated with index hospitalisation and inhospital mortality during the incident year. The most common causes of admission will be identified and contrasted with the most expensive hospitalised conditions. We will also calculate the ratio of inpatient costs incurred through admissions of ambulatory care sensitive conditions to the total inpatient expenditures. The magnitude of variation in costs and health service utilisation will be established by calculating the extremal quotient, the coefficient of variation and the Gini mean difference for estimates obtained through multilevel regression analyses. ETHICS AND DISSEMINATION: This study has been approved by Hamilton Integrated Research Ethics Board (ID#1715-C). The results of the study will be distributed through peer-reviewed journals. They also will be disseminated at research events in academic settings, national and international conferences as well as with presentations to provincial health authorities.

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)0.017
metaresearch head score (Gemma)0.010
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: Protocol · Consensus signal: Protocol
Teacher disagreement score0.188
Threshold uncertainty score0.379

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.010
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.007
Science and technology studies0.0080.002
Scholarly communication0.0030.001
Open science0.0040.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0120.002

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 head0.194
GPT teacher head0.557
Teacher spread0.363 · 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
GenreProtocol

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

Citations8
Published2017
Admission routes3
Has abstractyes

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