Achilles Tendon Injury and Seasonal Variation: An Analysis Using Google Trends
Bibliographic record
Abstract
Purpose: Achilles tendon injury is one of the most common sports-related injuries.Several studies suggest that Achilles tendon injury is associated with seasonal variation.The purpose of this study is to determine the relationship between seasonal variations and Achilles tendon injury through Google Trends (GT) and to evaluate the correlation between GT and actual data.Methods: We identified three articles through PubMed database as control group.The experimental group (GT group) was collected from GT by setting the same conditions as the control group.For GT group, we use the search terms related to the Achilles tendon injury.The exploration period was set from January 1, 2004 to December 31, 2018.Results: There is approximately more than 90% (p＜ 0.05) correlation between GT group and control group.The incidences of Ontario were the highest in the summer.Those of New York and Vancouver were higher in spring compared to those of Ontario.Conclusion: Our study implies that there is significant seasonal variation for Achilles tendon injury.Most of these injuries seem to occur in spring and summer.Also, there is a significant relationship between GT data and actual data.If the data from GT can be analyzed properly, these approach methods will be useful for epidemiological research.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.030 | 0.042 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".