Reaction Time Across the Menstrual Cycle: A Critically Appraised Topic
Bibliographic record
Abstract
Clinical Scenario: Reaction time is integral in many tasks during work, sport, and life, thus, alterations in reaction time may impact performance and injury risk. There are various factors that can influence reaction time, such as the physical state of the individual, including their age or sex. When comparing males and females, there is a major physiological difference to their physical state as hormones fluctuate during menstrual cycle phases, which not only affects the reproductive system, but females may experience physiological, cardiovascular, respiratory, or metabolic changes throughout their menstrual cycle phases. Therefore, this goal of this critically appraised topic is to examine whether reaction time changes during menstrual cycle phases. Focused Clinical Question: In healthy, eumenorrheic females, does reaction time change from one menstrual cycle phase to other menstrual cycle phases? Summary of Key Findings: Among the five studies evaluated in this CAT, all found significant changes to reaction time during phases of the menstrual cycle. Most studies found that reaction time was inversely related to sex hormone levels, indicating that phases with low hormone levels had longer reaction time than those phases with higher hormone levels; however, one study found reaction time to be prolonged or slower during the luteal phase, when hormone levels are higher. Clinical Bottom Line: Both auditory and visual reaction times vary across the menstrual cycle in healthy females with regular menstrual cycles (frequency and length). Given these findings, it is important to incorporate reaction time training across all phases of the menstrual cycle in female athletes. Strength of Clinical Recommendation: Based on the Strength of Recommendation Taxonomy, a Grade C is the strength of recommendation.
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 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 | 0.027 | 0.214 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".