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Record W4313014336 · doi:10.2196/39568

Peritoneal Dialysis Care in Mainland China: Nationwide Survey

2022· article· en· W4313014336 on OpenAlexvenueno aff
Ping Li, Xueying Cao, Weicen Liu, Delong Zhao, Sai Pan, Xuefeng Sun, Guangyan Cai, Jianhui Zhou, Xiangmei Chen

Bibliographic record

VenueJMIR Public Health and Surveillance · 2022
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePeritoneal dialysisTertiary careDialysisInternal medicineEmergency medicineFamily medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Peritoneal dialysis (PD) care in mainland China has been progressing in the past 10 years. OBJECTIVE: To complement information from the dialysis registry, a large-scale nationwide survey was conducted to investigate the current infrastructure and management of PD care at hospitals of different tiers. METHODS: A web-based multiple-choice questionnaire was distributed through the National Center for Nephrology Medical Quality Management and Control to PD centers of secondary and tertiary hospitals in October 2020. The 2-part survey collected the information of PD centers and the clinical management of patients on PD. A total of 788 effective surveys from 746 hospitals were voluntarily returned, and data were extracted and analyzed. RESULTS: The effective survey data covered 101,537 patients on PD, with 95% (96,460/101,537) in the tertiary hospitals. The median number of patients per PD center was 60 (IQR 21-152); this number was 32 (IQR 8-65) and 70 (IQR 27-192) for secondary and tertiary hospitals, respectively. There was a discrepancy in the availability of designated physical areas for different functions of PD care between the secondary and tertiary hospitals. The proportion of tertiary hospitals with PD training (P=.01), storage (P=.09), and procedure area (P<.001) was higher compared to secondary hospitals. PD catheter placement was performed in 96% (608/631) of the PD centers in tertiary hospitals, which was significantly higher compared to 86% (99/115) in secondary hospitals (P<.001). Automated PD was available in 55% (347/631) of the tertiary hospitals, which was significantly higher than that in secondary hospitals (37/115, 32%) according to the survey (P<.001). The most commonly performed PD module was continuous ambulatory peritoneal dialysis (772/788, 98%), followed by intermittent peritoneal dialysis (543/788, 69%). The overall reported nocturnal intermittent peritoneal dialysis was 31% (244/788); it was 28% (220/788) for continuous cycling peritoneal dialysis and 15% (118/788) for tidal peritoneal dialysis. Comparisons between the secondary and tertiary hospitals revealed no significant differences in prophylactic antibiotic use for PD catheter placement and therapeutic use for peritonitis. The first peritoneal equilibrium test was conducted in 58% (454/788) of patients at 4-6 weeks after initiation of PD, and 91% (718/788) reported at least one peritoneal equilibrium test per year. Overall, 79% (570/722) and 65% (469/722) of PD centers performed assessment for dialysis adequacy and residual kidney function, respectively; and 87% (685/788) of patients on PD were followed every 1 to 3 months for laboratory and auxiliary examinations. CONCLUSIONS: This national survey reflects the current status and disparities of PD center management in mainland China. The study results suggest that the PD care needs to be more conveniently accessible in secondary hospitals, and quality management and staff training in secondary hospitals are still in high demand.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.107

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.018
GPT teacher head0.296
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations12
Published2022
Admission routes1
Has abstractyes

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