Bibliographic record
Abstract
BACKGROUND: The use of diuretics, statins, and inhaled long-acting β2-agonists (LABAs) is linked to muscle cramps but largely by anecdotal evidence. This study sought population-level data to better evaluate these associations. METHODS: Linked health care databases containing prescribing information (December 1, 2000, to November 30, 2008) about 4.2 million residents of British Columbia, Canada, were evaluated using sequence symmetry analysis to determine in adults 50 years or older whether new quinine prescriptions (initiations of cramp treatment) increase in the year following diuretic, statin, or LABA starts. The statistic of interest was the sequence ratio: the number of quinine starts in the year following index drug introduction compared with the number of quinine starts in the preceding year (adjusted for age and time trends in population prescribing). RESULTS: Adjusted sequence ratios (95% CIs) for the 3 drug classes were 1.47 (1.33-1.63 [P < .001]) for diuretics, 1.16 (1.04-1.29 [P = .004]) for statins, and 2.42 (2.02-2.89 [P < .001]) for LABAs. For diuretic subclasses, adjusted sequence ratios (95% CIs) were 2.12 (1.61-2.78 [P < .001]) for potassium sparing, 1.48 (1.29-1.68 [P < .001]) for thiazidelike, and 1.20 (1.00-1.44 [P = .07]) for loop. For LABA subclasses, adjusted sequence ratios (95% CIs) were 2.17 (1.56-3.02) for LABAs alone and 2.55 (2.06-3.12) for LABAs-corticosteroids (P < .001 for both). CONCLUSIONS: Cramp treatment was substantially more likely in the year following introduction of LABAs, potassium-sparing diuretics, or thiazidelike diuretics, and 60.3% of quinine users (individuals experiencing cramp) received at least 1 of these medications during a 13-year period. In contrast, statin and loop diuretic associations were small. Physicians should be mindful that the use of these medications may worsen symptoms in patients experiencing nocturnal leg cramps.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".