Performance of waist-to-height ratio as a screening tool for identifying cardiometabolic risk in children: a meta-analysis
Bibliographic record
Abstract
Abstract Objective To provide the latest evidence of performance and robustness of waist-to-height ratio (WHtR) in discriminating clusters of cardiometabolic risk factors (CMRs) and promote WHtR in routine primary health care practice in children, a meta-analysis was used. Methods Searches was performed in eight databases from inception to July 03, 2020. Inclusion criteria were: (1) observational study, (2) children and adolescents, (3) provided WHtR measurements, (4) had CMRs as outcomes, and (5) diagnostic studies. Exclusion criteria were: (1) non-original articles, (2) unable to extract 2 × 2 contingency tables, (3) not in English or Chinese language, (4) populations comprising clinical patients, or (5) duplicate articles. WHtR cutoff points, 2 × 2 contingency tables were extracted from published reports. Outcomes included: CMR clusters of at least three CMRs (CMR 3 ), two (CMR 2 ), one (CMR 1 ), and CMR components. Bivariate mixed-effects models were performed to estimate the summarised area under the curves (AUSROC) with 95% CI s and related indexes. We conducted subgroup analyses by sex and East Asian ethnicity. Results Fifty-three observational studies were included. The AUSROC reached 0.91 (95% CI : 0.88–0.93), 0.85 (95% CI : 0.81, 0.88) and 0.75 (95% CI : 0.71, 0.79) for CMR 3 , CMR 2 , and CMR 1 , respectively. The pooled sensitivity and specificity for CMR 3 reached 0.84 and exceeded 0.75 for CMR 2 . For CMR 1 , the sensitivity achieved 0.55 with 0.84 for specificity. We had similar findings for our subgroup and sensitivity analyses. Conclusions WHtR shows good and robust performance in identifying CMRs clustering across racial populations, suggesting its promising utility in public health practice globally.
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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 | 0.033 | 0.063 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.085 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".