Association of Local Unit Sampling and Microbiology Laboratory Culture Practices With the Ability to Identify Causative Pathogens in Peritoneal Dialysis-Associated Peritonitis in Thailand
Bibliographic record
Abstract
INTRODUCTION: This describes variations in facility peritoneal dialysis (PD) effluent (PDE) culture techniques and local microbiology laboratory practices, competencies, and quality assurance associated with peritonitis, with a specific emphasis on factors associated with culture-negative peritonitis (CNP). METHODS: Peritonitis data were prospectively collected from 22 Thai PD centers between May 2016 and October 2017 as part of the Peritoneal Dialysis Outcomes and Practice Patterns Study. The first cloudy PD bags from PD participants with suspected peritonitis were sent to local and central laboratories for comparison of pathogen identification. The associations between these characteristics and CNP were evaluated. RESULTS: < 0.05). Marked variations were observed in PD center practices, particularly with respect to specimen collection and processing, which often deviated from International Society for Peritoneal Dialysis Guideline recommendations, and laboratory capacities, capabilities, and certification. Lower rates of CNP were associated with PD nurse specimen collection, centrifugation of PDE, immediate transfer of samples to the laboratory, larger hospital size, larger PD unit size, availability of an on-site nephrologist, higher laboratory capacity, and laboratory ability to perform aerobic cultures, undertake standard operating procedures in antimicrobial susceptibilities, and obtain local accreditation. CONCLUSION: There were large variations in PD center and laboratory capacities, capabilities, and practices, which in turn were associated with the likelihood of culturing and correctly identifying organisms responsible for causing PD-associated peritonitis. Deviations in practice from International Society for Peritoneal Dialysis guideline recommendations were associated with higher CNP rates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".