PD effluent specimen collection: Your questions answered
Bibliographic record
Abstract
When a patient on peritoneal dialysis (PD) presents with suspected PD-related peritonitis (e.g. cloudy PD fluid and abdominal pain), one of the most important initial aspects of management is for the nephrology nurse/home dialysis nurse to collect PD effluent specimens for white blood cells count, Gram stain, culture and sensitivity for inspection and to send for laboratory testing before antibiotics are started. A review by seven members of the International Society for Peritoneal Dialysis (ISPD) Nursing Committee of all 133 questions posted to the ISPD website 'Questions about PD' over the last 4 years (January 2018-December 2021), revealed 97 posted by nephrology nurses from around the world. Of these 97 questions, 10 were noted to be related to best practices for PD effluent specimen collection. For our review, we focused on these 10 questions along with their responses by the members of the ISPD 'Ask The Experts Team', whereby existing best practice recommendations were considered, if available, relevant literature was cited and differences in international practice discussed. We revised the original responses for clarity and updated the references. We found that these 10 questions were quite varied but could be organised into four categories: how to collect PD effluent safely; how to proceed with PD effluent collection; how to collect PD effluent for assessment; and how to proceed with follow-up PD effluent collection after intraperitoneal antibiotics have been started. In general, we found that there was limited evidence in the PD literature to answer several of these 10 questions posted to the ISPD website 'Questions about PD' by nephrology nurses from around the world on this important clinical topic of best practices for PD effluent specimen collection. Some of these questions were also not addressed in the latest ISPD Peritonitis Guidelines. Moreover, when polling members of our ISPD Nursing Committee we found when answering a few of these questions, nursing practice varied within and among countries. We encourage PD nurses to conduct their own research on this important topic, focusing on areas where research evidence is lacking.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 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".