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Record W2231835496 · doi:10.1007/s11999-015-4680-4

The Radiographic Union Score for Hip (RUSH) Identifies Radiographic Nonunion of Femoral Neck Fractures

2016· article· en· W2231835496 on OpenAlexaff
Tym Frank, Georg Osterhoff, Sheila Sprague, Alisha Garibaldi, Mohit Bhandari, Gerard P. Slobogean

Bibliographic record

VenueClinical Orthopaedics and Related Research · 2016
Typearticle
Languageen
FieldMedicine
TopicHip and Femur Fractures
Canadian institutionsMcMaster UniversityUniversity of British Columbia
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesCambridge University HospitalsUniversity of Cambridge
KeywordsMedicineNonunionRadiographyHip fractureBone healingSurgeryFemoral neckFemoral headOrthopedic surgeryOsteoporosisInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: The Radiographic Union Score for Hip (RUSH) is a previously validated outcome instrument designed to improve intra- and interobserver reliability when describing the radiographic healing of femoral neck fractures. The ability to identify fractures that have not healed is important for defining nonunion in clinical trials and predicting patients who will likely require additional surgery to promote fracture healing. We sought to investigate the utility of the RUSH score to define femoral neck fracture nonunion. QUESTIONS/PURPOSES: (1) What RUSH score threshold yields at least 98% specificity to diagnose nonunion at 6 months postinjury? (2) Using the threshold identified, are patients below this threshold at greater risk of reoperation for nonunion and for other indications? METHODS: A representative sample of 250 out of a cohort of 725 patients with adequate 6-month hip radiographs was analyzed from a multinational elderly hip fracture trial (FAITH). All patients had a femoral neck fracture and were treated with either multiple cancellous screws or a sliding hip screw. Two reviewers independently determined the RUSH score based on the 6-month postinjury radiographs and interrater reliability was assessed with the interclass correlation coefficient (ICC). There was substantial reliability between the reviewers assigning the RUSH scores (ICC, 0.81; 95% confidence interval [CI], 0.76-0.85). The RUSH score is a checklist-based system that quantifies four measures of healing: cortical bridging, cortical fracture disappearance, trabecular consolidation, and trabecular fracture disappearance.. Fracture healing was determined by two independent methods: (1) concurrently by the treating surgeon using both clinical and radiographic assessments as per routine clinical care; and (2) retrospectively by a Central Adjudication Committee using complete obliteration of the fracture line on radiographs alone. Receiver operating characteristic tables were used to define a RUSH threshold score that was > 98% specific for fracture nonunion. RESULTS: A threshold score of < 18 was associated with a 100% specificity (95% CI, 97%-100%) and a positive predictive value of 100% (95% CI, 73%-100%) for radiographic nonunion. In contrast, using the fracture healing assessments of the treating surgeons failed to identify a useful discriminatory nonunion threshold and the highest positive predictive value was 43%. With respect to complications, patients with RUSH scores below 18 had greater risk of undergoing reoperation for nonunion (reoperation when < 18: six of 13 [46%]; reoperation when ≥ 18: 11 of 237 [54%]; relative risk [RR], 9.9 [95% CI, 4.4-22.7]; p < 0.001) and for all indications (reoperation when < 18: eight of 13 [62%]; reoperation when ≥ 18: 54 of 237 [38%]; RR, 2.7 [95% CI, 1.7-4.4]; p = 0.004). CONCLUSIONS: The 6-month RUSH score is a reliable method for assessing radiographic healing. Our results highlight the discordance between radiographic determinations and clinician assessments of fracture healing and stress the need for clinical data to be incorporated in research studies evaluating fracture healing. LEVEL OF EVIDENCE: Level III, diagnostic study.

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)0.008
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.239
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.083
GPT teacher head0.432
Teacher spread0.349 · 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.

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

Citations80
Published2016
Admission routes1
Has abstractyes

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