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Management of Greater Trochanteric Pain Syndrome After Total Hip Arthroplasty: Practice Patterns and Surgeon Attitudes

2023· article· en· W4389304873 on OpenAlexaboutno aff
Daniel Axelrod, Seper Ekhtiari, Mitchell Winemaker, Justin de Beer, Thomas J. Wood

Bibliographic record

VenueJAAOS Global Research and Reviews · 2023
Typearticle
Languageen
FieldMedicine
TopicHip disorders and treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePalpationPhysical therapyBursitisPhysical examinationArthroplastyGreater trochanterHip painSurgeryFemur

Abstract

fetched live from OpenAlex

INTRODUCTION: Greater trochanteric pain syndrome (GTPS) or trochanteric bursitis is described as pain on the lateral side of the hip that does not involve the hip joint and can be elicited clinically by palpation over the greater trochanter. To date, there remains no consensus on clinical guidelines for either diagnosis or management of GTPS. METHODS: To understand the practice patterns, beliefs, and attitudes relating to the management of GTPS after total hip arthroplasty, a survey was developed and completed by Canadian arthroplasty surgeons. The final survey consisted of 23 questions divided into three sections: 1) screening questions; 2) demographic information; and 3) practice patterns, attitudes, and beliefs. RESULTS: Most surgeons use physical examination alone for diagnosis. A detailed analysis indicates that surgeons primarily treat GTPS with oral anti-inflammatories (57.1%), structured physiotherapy (52.4%), and steroid injections (45.2%). Management options are typically nonsurgical and comprise a combination of either unstructured or targeted physiotherapy, corticosteroid injections, or platelet-rich plasma. DISCUSSION: There remains an absence of clinical consensus for the diagnosis and management of GTPS after total hip arthroplasty. Physical examination is most often relied on, regardless of the availability of imaging aids. While common treatments of GTPS were identified, up to one-third of patients fail initial therapy.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.251
Threshold uncertainty score0.386

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.064
GPT teacher head0.393
Teacher spread0.329 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations6
Published2023
Admission routes1
Has abstractyes

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