Seasonal effects on the occurrence of nocturnal leg cramps: a prospective cohort study
Bibliographic record
Abstract
BACKGROUND: It has been anecdotally reported that nocturnal leg cramps in pregnant women are worse in summer. We analyzed population-level data to determine whether the symptom burden of nocturnal leg cramps is seasonal in the general population. METHODS: We examined time-series data for 2 independent measures of the symptom burden of leg cramps: (a) new quinine prescriptions (reflecting new or escalating treatment of leg cramps) from December 2001 to October 2007 among adults aged 50 years and older, which were obtained from linked health care databases that contain the prescribing information for the 4.2 million residents of British Columbia, Canada; and (b) the Internet search volume from February 2004 to March 2012 for the term "leg cramps" (reflecting public interest), which we obtained from Google Trends data and geographically limited to the United States and Australia. We assessed seasonality by determining how well a least-squares sinusoidal model predicted variability in the outcomes. RESULTS: New quinine prescriptions and Internet searches related to leg cramps were both seasonal, with highs in mid-summer and lows in mid-winter, and a peak-to-peak variability that was about two-thirds of the mean. Seasonality accounted for 88% of the observed monthly variability in new quinine prescriptions (p < 0.001) and 70% of the observed variability in Internet searches related to leg cramps (p < 0.001). INTERPRETATION: New quinine prescriptions and Internet searches related to leg cramps were seasonal and roughly doubled between the winter lows and summer highs. Why a disorder of peripheral motor neurons displays such strong seasonality warrants exploration.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".