Longitudinal study of chronic nausea and vomiting and its associations with sleep-related leg cramps in the US general population
Bibliographic record
Abstract
Introduction: Chronic nausea and vomiting (CNV) can have a long-term impact on people's health and quality of life. This study aims to investigate whether CNV is involved in the development of sleep-related leg cramps (SRLCs) in a longitudinal study involving 10,931 participants representative of the US general population. Methods: Participants were interviewed three years apart over the telephone using the Sleep-EVAL expert system. It collected information on sleep habits/disturbances (International Classification of Sleep Disorders), mental disorders (DSM-IV-TR) and medical conditions. CNV was defined as episodes of nausea and vomiting occurring at least five times a month for at least one month (outside pregnancy) as per the ROME IV recommendations. Results: At the initial interview, 3% (95%CI:2.7%-3.3%) of the participants reported CNV, and 2.5% (95%CI:2.2%-2.8%) at follow-up. The 3-year incidence for CNV was 1.4%. SRLCs was found in 12.4% (95%CI:11.9%-13.1%) of the sample at the first-interview and 11.5% (95%CI:10.1%-12.1%) at follow-up. Multivariate models show that individuals with CNV at both interviews had a relative risk 4.1 times higher (95%CI:2.7–6.2; p < 0.0001) to have SRLCs at follow-up compared to those without nausea or vomiting. Magnesium intake at the initial interview was a protective factor for SRLCs (RR 0.4; 95%CI: 0.2–0.7; p = 003). Discussion: Symptoms of CNV lasting for years is highly predictive of SRLC based on this longitudinal survey. The link between the two pathologies could be partially explained by potential potassium/magnesium depletion from muscles as a result of CNV. The findings of this study call for physician awareness of the association between CNV and SRLC.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".