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Abstract 34: Determinants of Successful Completion of Cardiac Rehabilitation When Secondary Prevention Programs Are Made Universally Accessible

2018· article· en· W2903640188 on OpenAlexaffabout
Stephanie J. Frisbee, Neville Suskin, Saverio Stranges, A. Pierce, Joseph Ricci

Bibliographic record

VenueCirculation Cardiovascular Quality and Outcomes · 2018
Typearticle
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsThe Scarborough HospitalWestern University
Fundersnot available
KeywordsMedicineReferralRehabilitationAmbulatoryPopulationIntervention (counseling)Emergency medicinePhysical therapyFamily medicineInternal medicineNursingEnvironmental health

Abstract

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Background: Cardiac rehabilitation programs (CRP) after a cardiovascular event are standard-of-care worldwide, though participation remains very low (estimated at 14%-35% in the US and <15% in Ontario). Removing health care system (HCS) barriers to accessing CRPs is essential to realize patient and population level health benefits, and to understand patient barriers to completion of CRP. Objectives: To develop a regional delivery system for CRP that removes HCS barriers to CRP so that patient factors affecting CRP participation and completion can be ascertained. Methods: One health care region (HCR) in Ontario implemented a regional CRP that was fully integrated into the HCS. The integrated CRP (HCS-I-CRP) automated referrals to an HCR-wide coordinating center which directed patients to a to a CRP in a local, community-based setting (LCB-CRP) within a 30-minute drive. The CRP was standardized across all LCB-CRPs. Information related to the referral, initiation, and completion of the CRP was collected and analyzed. Results: Referral and eligibility criteria were intentionally broad and included any cardiovascular admission within 1 year, or any cardiovascular intervention, procedure, or diagnosis. Automated inpatient and community referrals were received for cardiac or non-cardiac related hospitalizations (62% and 29%, respectively), or ambulatory health care visits (9%). The age distribution of patients referred from hospital and community sources was similar, though community referred patients were less complex (lower Johns Hopkins Adjusted Clinical Group (JH-ACG) category). In comparing the patient characteristics of those who completed vs. those who started but did not complete the CRP, the following patterns were observed: (1) completion rates were nearly identical across hospital and community all referral sources; (2) older patients were more likely to compete the CRP (72% for 70-89 years old vs. 57% for 40-49 years old); (3) while there were more referrals for men (61.5%) than women, the completion rates for men and women were nearly identical; (4) neighborhood income quintile of patients was not associated with completion of the CRP; (5) sicker patients were less likely to complete the CRP (higher resource utilization band, JH-ACG, and Charlson index); and (6) patients with heart failure were least likely to complete the CRP (58.8% vs. 66.8% for all other diagnoses). Conclusions: Our results suggest that an HCS-I-CRP with automatic referral to a LCB-CRP within a 30-minute drive removed traditional barriers to completing CRP: there were no socioeconomic or gender differences in CRP completion, and elderly patients had higher completion rates. Sicker patients were least likely to complete CRP. Mechanisms to identify these patients earlier and / or support their completion of these programs to reduce disease morbidity should be developed.

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)0.002
metaresearch head score (Gemma)0.009
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 score0.407
Threshold uncertainty score0.809

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.061
GPT teacher head0.370
Teacher spread0.309 · 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".

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Citations0
Published2018
Admission routes2
Has abstractyes

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