Healthcare contacts among patients lost to follow-up in HIV care: review of a large regional cohort utilizing electronic health records
Bibliographic record
Abstract
In the United States 40% of HIV patients are lost to follow-up (LTFU) following linkage to HIV care and an estimated 30–61% of new HIV transmissions are attributed to this group. To characterize those LTFU and healthcare contacts they make, we retrospectively analyzed a large regional HIV cohort in Calgary, Canada, utilizing a province-wide electronic health record. Adults engaged in HIV care between January 2010 and August 2014 who had >12 months without HIV clinic contact were identified as LTFU. Of 1928 individuals engaged in care, 176 became LTFU with 64% having no healthcare contacts, 20% receiving HIV care elsewhere, and 16% making non-HIV healthcare contacts. Those LTFU making non-HIV healthcare contacts did so a median of six times (interquartile range 2–8), 76% attending emergency departments (ED). Compared to those retained in care, LTFU patients were younger (median age 43 versus 47 years), had lower CD4 + cell counts (median 420 versus 500 × 10 6 /l) and more commonly resided outside of the centralized HIV clinic’s city (odds ratio 4.58) (all p < 0.01). Our finding that a majority of those LTFU did not make healthcare contacts suggests that community and HIV clinic-based relinkage programs are needed. For those LTFU who make healthcare contacts enhanced ED-based relinkage programs could engage a majority.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".