Effect of menstrual cycle phase, menstrual irregularities and hormonal contraceptive use on anterior knee laxity and non-contact anterior cruciate ligament injury occurrence in women: a protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
Exercising women report three to six times more ACL tears than men, which happen, in the majority of cases, with a non-contact mechanism. This sex disparity has, in part, been attributed to the differences in reproductive hormone profiles between men and women. Many studies have shown that anterior knee (AK) laxity and the rate of non-contact ACL injuries vary across the menstrual cycle, but these data are inconsistent. Similarly, several studies have investigated the potential protective effect of hormonal contraceptives on non-contact ACL injuries, but their conclusions are also variable. The purpose of this systematic review and meta-analysis is to, identify, evaluate and summarise the effects of endogenous and exogenous ovarian hormones on AK laxity (primary outcome) and the occurrence of non-contact ACL injuries (secondary outcome) in women. We will perform a systematic search for all observational studies conducted on this topic. Studies will be retrieved by searching electronic databases, clinical trial registers, author's personal files and cross-referencing selected studies. Risk of bias will be assessed using the Newcastle Ottawa Quality Assessment Scale for Cohort and Case-Control Studies. Certainty in the cumulative evidence will be assessed using the Grading of Recommendations Assessment, Development and Evaluation approach. The meta-analyses will use a Bayesian approach to address specific research questions in a more intuitive and probabilistic manner. This review is registered on the international database of prospectively registered systematic reviews (PROSPERO; CRD42021252365).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.032 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".