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Record W4414911102 · doi:10.1186/s12891-025-08708-7

Diagnosis and management of femoroacetabular impingement syndrome (FAIS): a survey of contemporary physiotherapy practice

2025· article· en· W4414911102 on OpenAlexaffabout
Peter R. Lawrenson, Helen French, Benita Olivier, Karen Barker, Joanne L. Kemp, Jackie L. Whittaker, Stephanie J. Woodley

Bibliographic record

VenueBMC Musculoskeletal Disorders · 2025
Typearticle
Languageen
FieldMedicine
TopicHip disorders and treatments
Canadian institutionsResearch CanadaUniversity of British Columbia
FundersAustralian Physiotherapy AssociationPhysiotherapy New Zealand
KeywordsFemoroacetabular impingementSports medicineOrthopedic surgeryRehabilitationRheumatologyImpingement syndrome

Abstract

fetched live from OpenAlex

BACKGROUND: Femoroacetabular impingement syndrome (FAIS) is a motion-related hip disorder characterised by altered hip-joint morphology and symptoms. Recent consensus statements have provided guidance on the diagnosis and management of FAIS but given the knowledge gaps in translating research into practice, it is unclear at what level this is being utilised by primary contact physiotherapists. This study undertook a cross-sectional multi-centre international survey to describe contemporary physiotherapy practice for the diagnosis and management of femoroacetabular impingement syndrome (FAIS). METHODS: An online survey comprising 32 questions based around current consensus recommendations for the diagnosis and management of FAIS, was developed. The survey was distributed to six English-speaking countries (Australia, Canada, Ireland, New Zealand, South Africa and the United Kingdom) where physiotherapists work as primary contact practitioners. Questions were answered with a 5-point Likert scale. To describe the 'most commonly' utilised tools for diagnosis and management, the two highest ranked responses ('always' and 'often') were combined for analysis and presented as a percentage of total respondents. RESULTS: Four hundred and twenty-nine (72%) of eligible respondents were included. Respondents varied across the six countries, 58% were female, and most worked in private practice (70%). When diagnosing FAIS, patient-reported signs/symptoms (90%), functional tests (88%), special tests (87%), and strength assessments (70%) were 'most commonly' used, while imaging (60%) and balance assessment (33%) were less frequently implemented. Most respondents employed strengthening exercises (97%) and education (96%) in their management of FAIS, and some utilised range of motion/stretching (62%), and manual therapy (62%). Half of the respondents (52%) use patient-reported outcome measures to assess treatment effectiveness. CONCLUSIONS: Our findings of physiotherapy diagnosis and management of FAIS from six countries broadly aligns with contemporary expert recommendations. Physiotherapy diagnosis of FAIS in practice is guided by patient-reported symptoms, and functional and special tests. Central to physiotherapy management is exercise and advice/education. Other modalities are less frequently utilised. CLINICAL TRIAL NUMBER: Not applicable.

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.336
Teacher spread0.315 · 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 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

Citations0
Published2025
Admission routes2
Has abstractyes

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