Female athlete triad syndrome: A bibliometric analysis
Bibliographic record
Abstract
Introduction: Menstrual disruption, lack of energy availability (with or without an eating disorder), and decreased bone mineral density are collectively known as the female athlete triad. It is common among young women who engage in athletic activities. This study aimed to identify the female athlete triad patterns and provide nutritional recommendations for female athletes to prevent triad syndrome. Methods: This study used a quantitative method with a bibliometric study approach. The inclusion criteria were document type ‘Article,’ publication stage ‘Fully published articles,’ source type ‘Journal,’ and language ‘English,’ from 2018-2024. Data were analysed using Scopus, VOSviewer, Nvivo 12 Plus, and Rstudio. Results: Research on female athlete triad syndrome showed a notable increase in 2014 and 2022. The United States (52 papers), Canada (ten papers), and Japan (nine papers) were the leading contributors. Five key clusters were identified: energy and metabolism, bone mineral density, menstrual disorders, sports injuries, and athlete performance. To effectively address the nutritional needs of female athletes and mitigate the risk of triad syndrome, it is essential to consider these five key clusters. The development of the triad in female athletes is primarily due to insufficient nutrition and calorie intakes, leading to a negative energy balance. Conclusion: There is still much to learn, but recent research has focused on minimising risks and maximising benefits for young female athletes by addressing the key clusters identified in this study. Healthcare professionals should educate patients, parents, and coaches about female athletes’ potential challenges and the best strategies to support them.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | high |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.015 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.196 | 0.212 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".