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Record W4416186461 · doi:10.1177/23259671251387342

The Use of Dual-Energy X-ray Absorptiometry (DEXA) in Evaluating Recovery After Musculoskeletal Injuries in Athletes: A Scoping Review

2025· article· en· W4416186461 on OpenAlexaff
Robert DiCesare, Umair Tahir, Atefeh Noori, Norah Matthies, Jaskarndip Chahal, Ryan Paul, Andrea Chan

Bibliographic record

VenueOrthopaedic Journal of Sports Medicine · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced X-ray and CT Imaging
Canadian institutionsAthletic Edge Sports MedicineHospital for Sick ChildrenToronto Western HospitalUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsRehabilitationSports medicineDual-energy X-ray absorptiometryOrthopedic surgeryMusculoskeletal injury

Abstract

fetched live from OpenAlex

Background: Dual-energy x-ray absorptiometry (DEXA) is a widely used imaging modality in sports medicine and orthopaedics due to its accuracy in assessing bone mineral density (BMD) and body composition. Tracking these parameters provides valuable insights into the recovery process after musculoskeletal injuries, which are prevalent among athletes. Purpose: To evaluate how DEXA imaging has been used to guide rehabilitation in both recreational and competitive athletes by monitoring changes in BMD and body composition after musculoskeletal injuries. Study Design: Scoping review; Level of evidence, 4. Methods: A comprehensive literature search was conducted in Ovid MEDLINE, EMBASE, Cochrane Central, and SportDISCUS databases (2000-2024). Studies were included if they used DEXA to measure BMD or body composition changes in athletes after musculoskeletal injuries. Titles, abstracts, and full texts were screened independently by 2 reviewers, with discrepancies resolved through discussion. Data were synthesized qualitatively, and major trends were reported and identified. Results: Of 1132 unique records, 12 studies met inclusion criteria, involving 319 athletes (34% female) with injuries such as anterior cruciate ligament (ACL) tears, lumbar stress fractures, femoroacetabular impingement, and Achilles tendon ruptures. Athletes represented a range of sports, including soccer, basketball, cricket, and triathlon, at both competitive and recreational levels. DEXA identified significant BMD declines after injury, particularly in the affected limb. For instance, up to a 7% BMD reduction was observed in surgical limbs after ACL reconstruction, persisting for up to 2 years in some cases. Rehabilitation strategies incorporating blood flow restriction therapy or combined running and isometric exercises better preserved lean mass and BMD compared with conventional programs focused solely on closed and open kinetic chain exercises. Conclusion: The review demonstrated that DEXA imaging is a promising tool in orthopaedic sports medicine for assessing injury-related changes in BMD and body composition. It provides detailed insights into recovery processes and aids in tailoring rehabilitation strategies and return-to-sport decisions. Some studies incorporated advanced rehabilitation methods such as blood flow restriction therapy, which appeared to accelerate recovery as measured by DEXA outcomes. Future studies should explore integrating DEXA findings into athlete-specific rehabilitation protocols with validated thresholds for return to play.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.925
Threshold uncertainty score0.621

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.016
GPT teacher head0.301
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 teacher head, not a consensus.

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