Assessment of metabolic risks for non-communicable diseases using Sasang constitution: a protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Sasang constitutional medicine (SCM), which categorizes humans into four Sasang types according to their constitution-specific characteristics, has been identified as being useful in predicting metabolic risks and preventing non-communicable diseases (NCDs). However, no systematic review has evaluated this relationship previously. This study protocol describes a method for evaluating the association between Sasang constitution and the metabolic risk factors for NCDs. METHODS: The following nine academic databases will be used as data sources for entries: Medical Literature Analysis and Retrieval System Online, Excerpta Medica database, Web of Science, and six Korean databases. All cohort, case-control, and cross-sectional studies that were published by December 2021 and could explain the association between Sasang constitution and metabolic risk factors for NCDs will be considered eligible. Two independent researchers will select studies, extract data, assess quality of studies, and qualitatively evaluate clinical evidence, subsequently. The quality assessment will be evaluated using the Newcastle-Ottawa Scale, with modifications if necessary. Quantitative data will be synthesized as a random-effects model, if applicable. The strength of clinical evidence will be performed applying the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) or GRADE-Confidence in Evidence from Reviews of Qualitative research approach. DISCUSSION: This study will contribute to helping clinicians and health authorities detect any relevant metabolic risks that patients may have, based on systematic clinical evidence. TRIAL REGISTRATION: Review Registry Unique Identifying Number: reviewregistry1213.
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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 | 0.088 | 0.139 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.018 | 0.020 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.066 | 0.008 |
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".