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Record W4399677111 · doi:10.1093/eurjpc/zwae175.083

Differential impact of ambulatory visits to specialist physicians on major cardiovascular events among incident cases of diabetes with or without known coronary heart disease: a cohort study

2024· article· en· W4399677111 on OpenAlexaff
S. O'Connor, Claudia Blais, Jérémie Sylvain-Morneau, Abdoulaye Diop, Miceline Mésidor, Jacinthe Leclerc, Paul Poirier

Bibliographic record

VenueEuropean Journal of Preventive Cardiology · 2024
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsUniversité LavalInstitut National de Santé Publique du QuébecInstitut universitaire de cardiologie et de pneumologie de Québec
Fundersnot available
KeywordsMedicineMaceMyocardial infarctionHazard ratioDiabetes mellitusAmbulatoryCohortEmergency medicineStroke (engine)Proportional hazards modelInternal medicineConfidence intervalConventional PCI

Abstract

fetched live from OpenAlex

Abstract Background Following a diagnosis of diabetes, specialist physicians may be solicited regarding the prevention of major cardiovascular events (MACE) (stroke, myocardial infarction, cardiovascular death). Patients with established coronary heart disease (CHD) are more susceptible to MACE and may require distinct attention. However, trajectories of visits to specialists are heterogenous and their efficacy preventing MACE remains unexplored. Purpose To assess whether trajectories of ambulatory visits to specialists are associated with MACE among new cases of diabetes, considering the presence or absence of CHD. Methods Using an integrated surveillance system of linked administrative databases dating back to 1996, we identified new diabetes cases aged ≥20 years during fiscal year 2013, with no history of MACE. Trajectory period: Individual trajectories based on the presence/absence of ≥1 ambulatory visit to a specialist (cardiologist/internist/endocrinologist) were compiled from diagnosis across consecutive 3-month periods, up to 2 years. Patients with no specialist visit constituted a "primary care" group, while others were grouped using latent class trajectory analysis. Patients experiencing a MACE during this period were excluded. Explanatory period: Stratifying by the presence or absence of CHD at the end of the trajectory period, we compared MACE risk between groups using a Cox proportional model, from year 2 post-diagnosis up to 12-31-2019, adjusted with inverse probability weighting and presented as adjusted hazard ratio (aHR) with 95% confidence intervals (CI). Results Among the 29,949 patients, we identified 4 trajectory groups (Figure 1). During the trajectory period, the proportion of patients who consulted ≥1 specialist was 64% with known CHD, compared to 35% without CHD. Among patients who met ≥1 specialist, those with CHD were more likely to have consulted a cardiologist (74%) than those without CHD (37%), while they were less likely to have met with an internist (37 vs 50%) or an endocrinologist (11 vs 31%). During the explanatory period, 796 MACE occurred among the 4,783 patients with CHD. Patients who had "early visits only" had a lower risk of MACE, while no difference was observed with "regular/frequent" and "regular/sparce" visits compared with the "primary care" group (Table 1). A total of 1,440 MACE occurred among the 25,166 patients without CHD. Patients with "regular/frequent" visits had no difference in the risk of MACE compared with the "primary care" group, while an increased risk of MACE was observed among those with "early visits only" and "regular/sparce visits". Conclusions While confounding by indication may persist, these results highlight the potential role of specialists in averting MACE in the context of CHD. However, the divergent risks of MACE according to the absence of CHD, visit frequency, and timing, advocate for improvement in the preventive role of specialists post-diabetes diagnosis.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.006
Threshold uncertainty score0.940

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.028
GPT teacher head0.371
Teacher spread0.343 · 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.

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
Published2024
Admission routes1
Has abstractyes

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