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Record W2107658823 · doi:10.1086/523010

Similar Challenges with Retention in Care Issues

2007· letter· en· W2107658823 on OpenAlexaff
Hartmut B. Krentz, R. A. Siemieniuk, M. John Gill

Bibliographic record

VenueClinical Infectious Diseases · 2007
Typeletter
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsAlberta Hip and Knee ClinicUniversity of Calgary
FundersU.S. Department of Veterans Affairs
KeywordsMedicineIntensive care medicine

Abstract

fetched live from OpenAlex

To the Editor—We read with interest the article by Giordano et al. [1]. We have conducted a similar retrospective analysis of HIV-infected patients within the Southern Alberta HIV Cohort. This program has a different demographic composition than that of the cohort described by Giordano et al. [1] but is similar to the US Department of Veteran Affairs, providing access with few financial barriers to a universal health care system. We included all patients with baseline CD4+ cell count data who initiated HAART from 1997 through 2005 (350 patients) and who had at least 1 subsequent clinic visit. Patients were followed up for at least 12 months. We stratified patients into 4 groups based on the number of clinic visits per year: <1.5, ⩾1.5 but <2.5, 2.5–3.5, and >3.5 visits per year. In contrast with the population treated by the US Department of Veterans Affairs, our population was younger (median age, 39 years) and predominantly white (68%), and 15% of our patients were female. In our population, 192 (55%) of patients were employed, 238 (68%) had at least a high school education, 59 (17%) were coinfected with hepatitis C virus, and 84 (24%) reported injection drug use. The median CD4+ cell count at HAART initiation was 195 cells/mm3 (interquartile range, 84–340 cells/mm3). Similar to Giordano et al. [1], we found profound gradients in some of the sociodemographic, clinical, and outcome variables. In our cohort, 62% of the patients attended >3.5 visits per year, 19% attended 2.5–3.5 visits per year, 13% attended ⩾1.5 but <2.5 visits per year, and 7% attended <1.5 visits per year. Our most economically disadvantaged population, Native or Aboriginal Canadians, exhibited the greatest disparity in clinic visits, with only 7% attending >3.5 visits per year and 25% attending <1.5 visits per year. Overall, 54% of patients with the least number of visits were injection drug users. Patients with lower CD4+ cell counts attended visits more regularly (P < .01). Patients who were employed also attended visits more regularly (P < .05). We did find a geographic gradient based on residency, with 85% of city residents attending >3.5 visits per year, compared with only 58% of rural residents. No statistically significant difference in mortality between the groups was seen, although very few deaths occurred (19 patients died). However, patients attending >3.5 visits per year had almost double the increase in CD4+ cell count (median increase, 129 cells/mm3 vs. 65 cells/mm3) and achieved an undetectable viral load more often than did patients with <1.5 visits per year (77% vs. 63%; P < .01). Our results, obtained from a cohort in a different country and under a different universal health care system, are remarkably similar to those reported by Giordano et al. [1]. These results suggest that, despite the absence of financial barriers, poor retention in HIV care is widely problematic and leads to worse outcomes. In Canada, low socioeconomic status and drug use are major contributors to inconsistent HIV care. We are in complete agreement with Giordano et al. [1] that optimal methods to retain these patients in care need to be developed if they are to achieve the full benefit of the health care opportunities available to them. Potential conflicts of interest. All authors: no conflicts.

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)0.023
metaresearch head score (Gemma)0.054
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.051
Threshold uncertainty score0.121

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.054
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0200.008
Scholarly communication0.0090.009
Open science0.0040.008
Research integrity0.0510.045
Insufficient payload (model declined to judge)0.0180.003

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 head0.342
GPT teacher head0.503
Teacher spread0.160 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations5
Published2007
Admission routes1
Has abstractno

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