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Record W2924339334 · doi:10.1136/bmjopen-2018-023957

Physicians’ patient base composition and mortality among people living with HIV who initiated antiretroviral therapy in a universal care setting

2019· article· en· W2924339334 on OpenAlexafffundabout
Beverly Allan, Kalysha Closson, Alexandra B. Collins, Mia Kibel, Shenyi Pan, Zishan Cui, Taylor McLinden, Surita Parashar, Viviane D. Lima, Jason Chia, Benita Yip, Rolando Barrios, Joan Montaner, Robert S. Hogg

Bibliographic record

VenueBMJ Open · 2019
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsUniversity of British ColumbiaBritish Columbia Centre on Substance UseSimon Fraser UniversityAIDS Vancouver
FundersNational Institute on Drug AbuseMichael Smith Health Research BCGovernment of the United Kingdom
KeywordsMedicineObservational studyProportional hazards modelAntiretroviral therapyHuman immunodeficiency virus (HIV)CohortCohort studyInternal medicinePediatricsDemographyFamily medicineViral load

Abstract

fetched live from OpenAlex

Objectives To assess the impact of physicians’ patient base composition on all-cause mortality among people living with HIV (PLHIV) who initiated highly active antiretroviral therapy (HAART) in British Columbia (BC), Canada. Design Observational cohort study from 1 January 2000 to 31 December 2013. Setting BC Centre for Excellence in HIV/AIDS’ (BC-CfE) Drug Treatment Program, where HAART is available at no cost. Participants PLHIV aged ≥ 19 who initiated HAART in BC in the HAART Observational Medical Evaluation and Research (HOMER) Study. Outcome measures All-cause mortality as determined through monthly linkages to the BC Vital Statistics Agency. Statistical analysis We examined the relationships between patient characteristics, physicians’ patient base composition, the location of the practice, and physicians’ experience with PLHIV and all-cause mortality using unadjusted and adjusted Cox proportional hazards models. Results A total of 4 445 PLHIV (median age = 42, Q1, Q3 = 34–49; 80% male) were eligible for our study. Patients were seen by 683 prescribing physicians with a median experience of 77 previously treated PLHIV in the past 2 years (Q1, Q3 = 23–170). A multivariable Cox model indicated that the following factors were associated with all-cause mortality: age (aHR = 1.05 per 1-year increase, 95% CI = 1.04 to 1.06), year of HAART initiation (2004–2007: aHR = 0.65, 95% CI = 0.53 to 0.81, 2008-2011: aHR = 0.46, 95% CI = 0.35 to 0.61, Ref: 2000–2003), CD4 cell count at baseline (aHR = 0.88 per 100-unit increase in cells/mm3, 95% CI = 0.82 to 0.94), and < 95% adherence in first year on HAART (aHR = 2.28, 95% CI = 1.88 to 2.76). In addition, physicians’ patient base composition, specifically, the proportion of patients who have a history of injection drug use (aHR = 1.11 per 10% increase in the proportion of patients, 95% CI = 1.07 to 1.15) or Indigenous ancestry (aHR = 1.07 per 10% increase , 95% CI = 1.03–1.11) and being a patient of a physician who primarily serves individuals outside of the Vancouver Coastal Health Authority region (aHR = 1.22, 95% CI = 1.01 to 1.47) were associated with mortality. Conclusions Our findings suggest that physicians with a higher proportion of individuals who face potential barriers to care may need additional supports to decrease mortality among their patients. Future research is required to examine these relationships in other settings and to determine strategies that may mitigate the associations between the composition of physicians’ patient bases and survival.

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)0.001
metaresearch head score (Gemma)0.006
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 score0.258
Threshold uncertainty score0.512

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.033
GPT teacher head0.356
Teacher spread0.323 · 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

Citations0
Published2019
Admission routes3
Has abstractyes

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