Qualitative, Patient-Centered Assessment of Muscle Cramp Impact and Severity
Bibliographic record
Abstract
BACKGROUND: There is an urgent need for new therapeutic options to treat muscle cramps; however, no patient-reported measures exist that capture the entire cramp experience. We conducted a qualitative study to assess the experience of patients suffering muscle cramps, aiming to understand what factors determine the impact cramps have in patients' lives to guide the development of a patient-centered outcome measure of cramp severity and impact. METHODS: We enrolled patients with cramps due to several etiologies, including motor neuron disease, pregnancy-induced cramps, cirrhosis and hemodialysis, and idiopathic and exercise-induced cramps. Patients participated in semistructured interviews about their experiences with muscle cramps and their responses were recorded and transcribed. Data were analyzed with content analysis using data saturation to determine the sample size. We subsequently developed a conceptual framework of cramp severity and overall cramp impact. RESULTS: Ten patients were interviewed when data saturation was reached. The cramp experience was similar across disease and physiological states known to cause muscle cramps. The main themes that compose the overall cramp impact are cramp characteristics, sleep interference, daytime activities interference, and the effect on mental health. CONCLUSIONS: This framework will be used to develop a patient-reported outcome of cramp severity and impact.
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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 | 0.028 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".