Where is Your Pain? Achilles Tendinopathy Pain Location on Loading Is Different to Palpation, Imaging and Recall Location
Bibliographic record
Abstract
OBJECTIVE: To describe and compare pain maps reported during Achilles tendon loading exercises with recall pain location, in people with pain on palpation in their Achilles tendon and tendon pathology on imaging. DESIGN: Cross-sectional analysis of baseline RCT. METHOD: Participants were recruited from a larger Achilles tendinopathy clinical trial. Inclusion criteria were at least 2-month self-reported history of Achilles tendinopathy, midtendon palpation pain, and pathology on ultrasound tissue characterization. Participants were asked to identify their Achilles tendon pain location on a pain map with 8 prespecified locations while at rest prior to loading (recall pain), and subsequently during tendon loading exercises (loading pain). Participants could select multiple locations or select “other” if the locations did not represent their pain. RESULTS: Ninety-three participants were included (93% of participants from a clinical trial). The locations of pain on loading were diverse; all 8 pain locations (and an “other” option) were represented within this sample. Twenty-five percent of participants did not report pain with loading (n = 23 of 93). Of the 70 participants with loading pain, recall pain location differed to loading pain location in 40% (n = 28 of 70) of the participants. CONCLUSION: Palpation pain location, recall pain location, or location of pathology on imaging were not valid proxies for load-related pain in the Achilles tendon. How different pain locations respond to treatment is unknown. Some pathologies (eg, plantaris) have clear pain locations (eg, medial tendon), and assessing pain location may assist differential diagnosis. J Orthop Sports Phys Ther 2024;54(1):86-94. Epub 7 December 2023. doi:10.2519/jospt.2023.12131
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".