Hey Google? When Patients Consult Dr Google About Tendinopathy. An Analysis of Search Terms and Questions From 4 English-Speaking Regions Across 6 Tendons
Bibliographic record
Abstract
OBJECTIVE: To explore the most common search terms and questions in Google relating to Achilles, patellar, shoulder, elbow, hamstring, and gluteal tendons across 4 English-speaking regions, and determine the most common landing pages for tendon-based searches. DESIGN: Web-based data appraisal. METHODS: Tendon-related search terms, inputted by users into the Google search engine, were quantified globally, and subgrouped for 4 English-speaking regions – Australia, United States of America, United Kingdom, and Canada. Search questions related to tendons were searched globally. RESULTS: Six tendons were searched 2 416 438 times per month globally. Uptake of consensus recommendations were most consistent for the gluteal tendon, where “tendinopathy” was ranked one of the top 20 keyword search terms globally. Tendinopathy did not feature in any of the top patella, elbow, or hamstring-related searches, and vast differences were noted across English-speaking regions for Achilles and shoulder tendon-related searches. Search questions focused on curative treatment globally. CONCLUSION: Words matter. Uptake of consensus recommended terminology is poor, and practitioners have a role to play in translating consensus recommendations into the public domain. JOSPT Open 2024;2(3):233-239. Epub 20 May 2024. doi:10.2519/josptopen.2024.1129
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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 | 0.006 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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, 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".