MétaCan
Menu
Back to cohort
Record W4408446886 · doi:10.1093/rap/rkaf033

Osteoarthritis after major combat trauma: the Armed Services Trauma Rehabilitation Outcome Study

2025· article· en· W4408446886 on OpenAlexaff
Fearghal P. Behan, Alexander N. Bennett, Fraje Watson, Susie Schofield, Eleanor F. Miller, Oliver O’Sullivan, Christopher J. Boos, Nicola T. Fear, Paul Cullinan, Philip G. Conaghan, Anthony M. J. Bull, Maria‐Benedicta Edwards, Helen Blackman, Melanie Chesnokov, Emma Coady, Sarah Evans, Guy Fraser, Meliha Kaya-Barge, Maija Maskuniitty, David Pernet, Helen Prentice, Urszula Pucilowska, Stefan Sprinckmoller, Lajli Varsani, Anna Verey, Molly Waldron, Danny Weston, Tass White, Seamus Wilson, Louise Young, Dan Dyball, I Gibb, David Gray, Edward Sellon

Bibliographic record

VenueRheumatology Advances in Practice · 2025
Typearticle
Languageen
FieldMedicine
TopicTrauma and Emergency Care Studies
Canadian institutionsTrinity College
Fundersnot available
KeywordsMedicineOsteoarthritisRehabilitationPhysical therapyAlternative medicinePathology

Abstract

fetched live from OpenAlex

Abstract Objective To investigate the differences in clinical and radiographic knee OA markers between injured and uninjured UK service personnel. Methods This study was a cross-sectional analysis, 8 years post-injury, of a prospective cohort study. The Knee Injury and Osteoarthritis Outcome Scores (KOOS), radiographic Kellgren and Lawrence (KL) scores and Osteoarthritis Research Society International scores (joint space narrowing, sclerosis, osteophytes) were obtained from 565 uninjured and 579 matched (on sex, age, rank, regiment and role on deployment) major combat injured participants from the Armed Services Trauma Rehabilitation Outcome study; 35 had a knee injury and 142 had an amputation without knee injury. Kruskal–Wallis tests were used to compare between groups for KOOS and radiographic measures. A multiple logistic regression was performed on the effects of injury on radiographic features. Results The mean age at injury was 25.7 years (s.d. 5.2). Injured participants demonstrated worse KOOS values for pain {median 89 [interquartile range (IQR) 72–100] vs 94 [83–100]} and symptoms [median 80 (IQR 60–90) vs 85 (70–95), P < 0.001] and higher scores for radiographic variables than uninjured participants. Injured non-amputated/non-knee-injured participants had worse KOOS values than uninjured participants [pain: 92 (IQR 75–100) vs 94 (83–100); symptoms: 80 (IQR 60–90) vs 85 (70–95), P < 0.01]. Knee-injured participants had worse KOOS values [pain: 67 (IQR 55–85), symptoms: 55 (IQR 35–73), P < 0.001] than all subgroups and worse radiographic measures than injured non-amputated participants. KL score (≥1) and sclerosis were worse for amputees than injured non-amputated participants. Amputees had 4.04-fold increased odds (95% CI 2.45, 6.65) vs uninjured participants and knee-injured participants had 4.06-fold increased odds (95% CI 1.89–8.74) than uninjured participants of knee osteoarthritis (KOA; KL ≥1). Injured participants (without knee injury/amputation) had 1.74-fold (95% CI 1.27, 2.69) increased odds of KOA than uninjured participants. Conclusion Major combat trauma (in addition to knee injury or amputation) has a substantial effect on the development of KOA.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.084
Threshold uncertainty score0.799

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.007
GPT teacher head0.343
Teacher spread0.336 · 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

Citations5
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueRheumatology Advances in PracticeSame topicTrauma and Emergency Care StudiesFrench-language works237,207