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Construction of the Chinese Veteran Clinical Research (CVCR) Platform for the assessment of non-communicable diseases

2014· article· en· W2417455956 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueChinese Medical Journal · 2014
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Health and Risk Factors
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineFamily medicineMental healthNon-communicable diseaseCross-sectional studyGerontologyPsychiatryPublic healthNursing

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.118
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.051
GPT teacher head0.471
Teacher spread0.421 · 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