<i>Mycobacterium tuberculosis</i> peritonitis in peritoneal dialysis patients: A scoping review
Bibliographic record
Abstract
BACKGROUND: The clinical syndrome of Mycobacterium tuberculosis (M. tuberculosis) peritoneal dialysis (PD) peritonitis is poorly understood. Whether local tuberculosis (TB) patterns modify the clinical syndrome, and what factors associate with poor outcomes is also unknown. METHODS: A scoping review identified published cases of TB PD peritonitis. Cases from low- and high-TB burden areas were compared, and cases that did or did not suffer a poor clinical outcome were compared. RESULTS: There were 216 cases identified. Demographics, presentation, diagnosis, treatment and outcomes were described. Significant delays in diagnosis were common (6.1 weeks) and were longer in patients from low-TB burden regions (7.3 vs. 3.7 weeks). In low-TB burden areas, slower diagnostic methods were more commonly used like PD fluid culture (64.3% vs. 32.7%), and treatment was less likely with quinolone antibiotics (6.9% vs. 34.1%). Higher national TB incidence and lower GDP per capita were found in cases that suffered PD catheter removal or death. Diagnostic delays were not longer in cases in which a patient suffered PD catheter removal or death. Cases that suffered death were older (51.9 vs. 45.1 years) and less likely female (37.8% vs. 55.7%). Removal of PD catheter was more common in cases in which a patient died (62.0% vs. 49.1%). CONCLUSIONS: Outcomes in TB PD peritonitis are best predicted by national TB incidence, patient age and sex. Several unique features are identified to alert clinicians to use more rapid diagnostic methods that might enhance outcomes in TB PD peritonitis.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".