Enhancing healthcare quality and outcomes for peritoneal dialysis patients in Thailand: An evaluation of key performance indicators and <scp>PDOPPS</scp> cohort representativeness
Bibliographic record
Abstract
AIM: To assess whether the peritoneal dialysis (PD) centres included in the Peritoneal Dialysis Outcomes and Practise Patterns Study (PDOPPS) in Thailand are representative of other PD centres in the country, based on 8 key performance indicators (KPIs 1-8). METHODS: A retrospective analysis was conducted comparing PD-related clinical outcomes between PD centres included in the PDOPPS (the PDOPPS group) and those not included (the non-PDOPPS group) from January 2018 to December 2019. Logistic regression analysis was used to identify predictors associated with achieving the target KPIs. RESULTS: Of 181 PD centres, 22 (12%) were included in the PDOPPS. PD centres in the PDOPPS group were larger and tended to serve more PD patients than those in the non-PDOPPS group. However, the process and outcome KPIs (KPIs 1-8) were comparable between the 2 groups. Large hospitals (≥120 beds), providing care to ≥100 PD cases and having experience for >10 years were independent predictors of achieving the peritonitis rate target of <0.5 episodes/year. Most PD centres in Thailand showed weaknesses in off-target haemoglobin levels and culture-negative peritonitis rate. CONCLUSIONS: The PD centres included in Thai PDOPPS were found to be representative of other PD centres in Thailand in terms of clinical outcomes. Thus, Thai PDOPPS findings may apply to the broader PD population in Thailand.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".