Characteristics of and treatment outcomes in rifampicin-intolerant patients
Bibliographic record
Abstract
SUMMARY BACKGROUND Rifampicin (RIF) is considered the backbone of TB treatment, but adverse effects often limit its use. METHODS This retrospective cohort study examined patients treated for TB disease at our institution, and compared those who received RIF to those who were intolerant to RIF. RESULTS A total of 829 patients were included. Seventy-six patients (9%) were intolerant to RIF. Patients with RIF intolerance were significantly older (median age: 67 years, IQR 50–78 vs. 48 years, IQR 31–70; P < 0.0001), and were more likely to be female (57% vs. 41%; P = 0.01) and have concurrent diabetes mellitus (37.3% vs. 19%; P < 0.0001) compared to those who tolerated RIF. RIF intolerance was most commonly due to transaminitis (25%), cytopenia (14.5%), rash (17.1%) and gastro-intestinal intolerance (7.8%). Twenty patients were subsequently challenged with rifabutin, and this was successful in 70%. The mean treatment duration was significantly longer in patients who were intolerant to RIF (335 vs. 270 days; P < 0.001). There was no significant difference in treatment outcomes. CONCLUSION RIF intolerance is more common in older patients, females, and those with concurrent diabetes mellitus. Patients who could not tolerate RIF had a longer duration of therapy, but no difference in treatment outcomes. When attempted, rifabutin was well tolerated in most patients with a previous RIF-related adverse event.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".