Real-world weight changes in people with HIV-1 at risk of weight gain (female, Black or Hispanic) switching from integrase strand transfer inhibitors
Bibliographic record
Abstract
Aim:Compare weight changes between people living with HIV-1 (PLWH) at high risk of weight gain (females, Blacks or Hispanics) switching from an integrase strand transfer inhibitor (INSTI) to a protease inhibitor (PI) or another INSTI.Materials & methods:Mean weight changes from pre-switch to up-to-12 months post-switch were retrospectively compared between PLWH switching to a PI or INSTI.Results:356 PLWH were eligible. At 9- and 12-month post-switch, weight increases were observed for INSTI (weight: +1.55 kg and +1.59 kg), while decreases were observed for PI (-0.23 kg and -1.59 kg); differences between cohorts widened over time.Conclusion:These data suggest that switching off an INSTI may be a management tool to mitigate or reverse weight gain.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".