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Record W2944108905 · doi:10.1186/s12906-019-2491-y

Traditional Chinese medicine use is associated with lower end-stage renal disease and mortality rates among patients with diabetic nephropathy: a population-based cohort study

2019· article· en· W2944108905 on OpenAlexaff
Hsing‐Yu Chen, Heng‐Chih Pan, Yung-Chang Chen, Yu‐Chun Chen, Yi-Hsuan Lin, Sien‐Hung Yang, Jiun-Liang Chen, Hau‐Tieng Wu

Bibliographic record

VenueBMC Complementary and Alternative Medicine · 2019
Typearticle
Languageen
FieldMedicine
TopicChronic Kidney Disease and Diabetes
Canadian institutionsUniversity of Toronto
FundersMinistry of Science and Technology, TaiwanChang Gung Medical Foundation
KeywordsMedicineHazard ratioPropensity score matchingEnd stage renal diseaseInternal medicineCumulative incidenceConfidence intervalProportional hazards modelCohort studyCohortPopulationConfoundingDiabetes mellitusIncidence (geometry)Rate ratioDiseaseEnvironmental healthEndocrinology

Abstract

fetched live from OpenAlex

BACKGROUND: Diabetic nephropathy (DN) is a common complication of diabetes mellitus (DM) that imposes an enormous burden on the healthcare system. Although some studies show that traditional Chinese medicine (TCM) treatments confer a protective effect on DN, the long-term impact remains unclear. This study aims to examine end-stage renal disease (ESRD) and mortality rates among TCM users with DN. METHODS: A total of 125,490 patients with incident DN patients from 2004 to 2006 were identified from the National Health Insurance Research Database in Taiwan and followed until 2012. The landmark method was applied to avoid immortal time bias, and propensity score matching was used to select 1:1 baseline characteristics-matched cohort. The Kaplan-Meier method and competing-risk analysis were used to assess mortality and ESRD rates separately. RESULTS: Among all eligible subjects, about 60% of patients were classified as TCM users (65,812 TCM users and 41,482 nonusers). After 1:1 matching, the outcomes of 68,882 patients were analyzed. For the ESRD rate, the 8-year cumulative incidence was 14.5% for TCM users [95% confidence interval (CI): 13.9-15.0] and 16.6% for nonusers (95% CI: 16.0-17.2). For the mortality rate, the 8-year cumulative incidence was 33.8% for TCM users (95% CI: 33.1-34.6) and 49.2% for nonusers (95% CI: 48.5-49.9). After adjusting for confounding covariates, the cause-specific hazard ratio of ESRD was 0.81 (95% CI: 0.78-0.84), and the hazard ratio of mortality for TCM users was 0.48 (95% CI: 0.47-0.50). The cumulative incidence of mortality increased rapidly among TCM users with ESRD (56.8, 95% CI: 54.6-59.1) when compared with TCM users without ESRD (30.1, 95% CI: 29.4-30.9). In addition, TCM users who used TCM longer or initiated TCM treatments after being diagnosed with DN were associated with a lower risk of mortality. These results were consistent across sensitivity tests with different definitions of TCM users and inverse probability weighting of subjects. CONCLUSIONS: The lower ESRD and mortality rates among patients with incident DN correlates with the use of TCM treatments. Further studies about specific TCM modalities or medications for DN are still needed.

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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.030
GPT teacher head0.290
Teacher spread0.260 · 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".

Quick stats

Citations58
Published2019
Admission routes1
Has abstractyes

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