The Efficacy of Calcium Carbonate in the Treatment of Protease Inhibitor-Induced Persistent Diarrhea in HIV-Infected Patients
Bibliographic record
Abstract
BACKGROUND: Although some evidence exists to support the practice of using calcium carbonate to treat nelfinavir-induced diarrhea, there is a lack of data supporting the role of calcium in diarrhea induced by other protease inhibitors (PIs). PURPOSE: The objective of this prospective open-label study is to evaluate the efficacy of calcium carbonate in the treatment of PI-induced persistent diarrhea in HIV-infected patients. METHOD: Along with dietary advice, patients were asked to take oral calcium carbonate 500 mg twice daily for 2 weeks. Visual Analog Scale (VAS) and the National Cancer Institute of Canada (NCIC) scale were used to assess the severity of diarrhea. Data were analyzed using paired t tests to test for differences in VAS and NCIC scores between baseline and 14 days. Pearson correlation was used to explore the relationships between change in diarrhea and patient baseline factors. RESULTS: At day 0, the mean VAS +/- standard deviation was 6.6 +/- 2.1 and decreased to 5.3 +/- 1.9 (p=.01) after 14 days. At day 0, the mean NCIC score was 1.9 +/- 0.8 and decreased to 1.2 +/- 0.9 (p=.005) after 14 days. No baseline patient factors predicted change in NCIC or VAS grade. CONCLUSION: Calcium carbonate is associated with a reduction of diarrhea in individuals with diarrhea induced by PI.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".