Adverse Health Effects for Individuals Who Move Between HIV Care Centers
Bibliographic record
Abstract
BACKGROUND: Studies on patient mobility have focused on patients who become lost-to-follow-up (LTFU). Much less is known about patients who move with a planned transfer of care from one HIV center to another. We assess disease progression in patients who moved and then returned to our care compared with patients remaining or were LTFU. METHODS: We identified which patients left our HIV care program between January 01,2000, to January 01,2008, defined how they left (either moved or LTFU), and then determined the health status of returning patients. We examined the impact of the move on their health by comparing clinical measurements (eg, CD4, new AIDS) at their departure and on return. RESULTS: Forty-four percent of all patients left care; 38% of these returned. In contrast to those remaining in local care whose CD4 counts climbed, "moved" patients exhibited deterioration in both CD4 counts and incident AIDS comparable to LFTU patients. Only 1 in 3 patients who moved had our medical records requested by a new HIV center. CONCLUSIONS: We suspect that despite forward planning, a move may result in potential serious interruptions and/or disengagements of care. The potential harmful health effects can in some be equivalent becoming LTFU. Recognizing and addressing the potential disruption in care from a planned move may be of value in improving outcomes.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".