Physical agents' level in women with primary dysmenorrhea: A cross‐sectional observational study
Bibliographic record
Abstract
Abstract Objective Primary dysmenorrhea (PD), menstrual pain in the absence of pathology, is the main reason for gynecological consultation in young women. Physical agents, used in physiotherapy for therapeutic purposes, can be self‐used by women with primary dysmenorrhea. The objective of this study is to determine the best‐known and most used physical agents. The study also investigates pain intensity, perceived pain, and the areas of most frequent painful. Methods An observational, cross‐sectional study was carried out. The sample was a cohort of adult female population of students or university researchers from the University of A Coruña (Spain) with primary dysmenorrhea. Data collection instrument was an online self‐administered questionnaire. Main variables were pain intensity, description and location (Numeric Rating Scale and McGill Pain Questionnaire, short version), and physical agents used (self‐reported). Kendall's coefficient of concordance, Spearman's correlation coefficient, the χ 2 ‐test, the Mann–Whitney U ‐test, and the Kruskal–Wallis test were used for statistical analysis. Results Among 736 respondents, 216 were considered PD cases. Heat, postures, and massage are the most well‐known physical agents (99.07%, 214 women; 85.6%, 185 women; 79.2%, 171 women, respectively). They are also the most used (heat, 92.1%, 199 women; postures, 81.9%, 177 women; 50 massage, 75.5%, 163 women). The abdomen, rachis, and lumbopelvic area were the most common pain perception locations (99%, 214 women). Conclusion Heat, postures, and massage were the most used, most recommended, and most effective physical agents. Physical exercise was the least known but the fourth most effective and the sixth most widely used physical agent. Impact This research provides relevant information from which to develop future intervention strategies. Knowing the most painful areas is also relevant to identify anatomical locations that can be the object of therapeutic attention.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 | 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".