The association between weight fluctuation and all-cause mortality
Bibliographic record
Abstract
BACKGROUND: Many observational studies have reported an association between weight fluctuation and all-cause mortality. However, the conclusions obtained from these studies have been unclear. OBJECTIVE: The current meta-analysis aimed to clarify the association between weight fluctuation and all-cause mortality. DATA SOURCE: We electronically searched PubMed, Embase, and Web of Science for articles reporting an association between weight fluctuation and all-cause mortality that were published before April 30, 2018. STUDY APPRAISAL AND SYNTHESIS METHODS: The methodological quality of each study was appraised using the modified Newcastle Ottawa Quality Assessment Scale. The hazard ratios (HRs) and corresponding 95% confidence intervals (CIs) were extracted from the included studies and pooled using random-effect models. Meta-regression approaches were also performed to explore sources of between-study heterogeneity. RESULTS: A total of 15 studies were eligible for the current meta-analysis. The pooled overall HR for all-cause mortality in the group with the greatest weight fluctuations compared with the most stable weight category was 1.45 (95% CI: 1.29-1.63). Considerable between-study heterogeneity was observed, some of which was partially explained by the different follow-up durations used by the included studies. Moreover, publication bias that inflated the risk of all-cause mortality was detected using Egger's test (P = .001). CONCLUSION: Weight fluctuation might be associated with an increased risk of all-cause mortality.
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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 | 0.013 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.011 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".