Construction of the Chinese Veteran Clinical Research (CVCR) Platform for the assessment of non-communicable diseases
Bibliographic record
Abstract
BACKGROUND: Based on the excellent medical care and management system for Chinese veterans, as well as the detailed medical documentation available, we aim to construct a Chinese Veteran Clinical Research (CVCR) platform on non-communicable diseases (NCDs) and carry out studies of the primary disabling NCDs. METHODS: The Geriatric Neurology Department of Chinese People's Liberation Army General Hospital and veterans' hospitals serve as the leading and participating units in the platform construction. The fundamental constituents of the platform are veteran communities. Stratified typical cluster sampling is adopted to recruit veteran communities. A cross-sectional study of mental, neurological, and substance use (MNS) disorders are performed in two stages using screening scale such as the Mini-Mental State Examination and Montreal cognitive assessment, followed by systematic neuropsychological assessments to make clinical diagnoses, evaluated disease awareness and care situation. RESULTS: A total of 9 676 among 277 veteran communities from 18 cities are recruited into this platform, yielding a response rate of 83.86%. 8 812 subjects complete the MNS subproject screening and total response rate is 91.70%. The average participant age is (82.01±4.61) years, 69.47% of veterans are 80 years or older. Most participants are male (94.01%), 83.36% of subjects have at least a junior high school degree. The overall health status of veterans is good and stable. The most common NCD are cardiovascular disorders (86.44%), urinary and genital diseases (73.14%), eye and ear problems (66.25%), endocrine (56.56%) and neuro-psychiatric disturbances (50.78%). CONCLUSION: We first construct a veterans' comprehensive clinical research platform for the study of NCDs that is primarily composed of highly educated Chinese males of advanced age and utilize this platform to complete a cross-sectional national investigation of MNS disorders among veterans. The good and stable health condition of the veterans could facilitate the long-term follow-up studies of NCDs and provide prospective data to the prevention and management of NCDs.
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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 | 0.017 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".