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Record W4416767196 · doi:10.4103/fjmd.fjmd-d-24-00042

Extracorporeal Shockwave Therapy for Popliteal Tendon Impingement Post-total Knee Replacement: A Case Report of Successful Nonsurgical Management

2025· article· en· W4416767196 on OpenAlexaboutno aff
Yang-Yi Wang, Yuan-Hsin Tsai, I‐Tsang Chiang, Ming-Chou Ku

Bibliographic record

VenueFormosan Journal of Musculoskeletal Disorders · 2025
Typearticle
Languageen
FieldMedicine
TopicTendon Structure and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsExtracorporeal shockwave therapyOsteoarthritisKnee painEffusionRange of motionKnee JointVisual analogue scaleExtracorporeal

Abstract

fetched live from OpenAlex

Popliteal tendon impingement (PTI) is a rare and often overlooked cause of persistent pain and functional limitations after total knee replacement (TKR). Conventional treatments provide limited relief. A 78-year-old male previously underwent cruciate-retaining TKR with restricted kinematic alignment to treat Grade 3 knee osteoarthritis. Despite appropriate intra- and postoperative care, he developed persistent posterolateral knee pain and effusion 3 months postoperatively. Dynamic ultrasonography revealed effusion around the popliteal tendon without bony spurs or cementophytes. After 6 months of unsuccessful conservative management, the patient received five sessions of weekly focused extracorporeal shockwave therapy (ESWT) targeting the posterolateral knee. Three months later, the patient demonstrated improvements in the Visual Analog Scale score (from 5 to 1), knee range of motion (0°–122° to 0°–130°), Western Ontario and McMaster Universities Osteoarthritis Index score (16 to 8), and Oxford Knee Score (27 to 37). This case demonstrates the potential efficacy of focused ESWT as a noninvasive alternative for managing PTI following TKR.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0030.002
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.007
GPT teacher head0.292
Teacher spread0.285 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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