Modelling clinical progression and health care utilization of <scp>HIV</scp>‐positive patients in <scp>B</scp>ritish <scp>C</scp>olumbia prior to death
Bibliographic record
Abstract
OBJECTIVES: The extent to which clinical progression of HIV-positive patients leads to an increase in health care utilization, especially prior to their death, is unknown. Thus, we modelled trends in CD4 cell count and emergency department utilization and the likelihood of an emergency department visit leading to a transfer to an acute care-level facility prior to a patient's death from nonaccidental causes. METHODS: Eligible patients initiated highly active antiretroviral therapy (HAART) in British Columbia between August 1996 and June 2006 (n = 457). Patients were followed until their death, which occurred on or before 30 June 2007 (period in which the emergency department visit data were available). Trends were modelled using generalized mixed effects. RESULTS: Patients experienced a significantly steep decline in CD4 cell count and a corresponding increase in the number of emergency department visits and transfers to acute-level facilities in the 5 years prior to death. For every 6-month interval prior to death, the CD4 cell count decreased by 13.22 cells/μL, the risk of experiencing an emergency department visit increased by 9%, and among those ever admitted, the odds ratio of being transferred to an acute care-level facility increased by 3%. CONCLUSIONS: We showed that patients experienced a steep decline in CD4 cell count, which was associated with an increase in health care utilization prior to their death. These findings highlight the substantial residual avoidable burden that unsuccessfully managed HIV disease poses, even in the HAART era. Further strategies to enhance sustained and successful engagement in care are urgently needed to mitigate high health care utilization.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".