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Record W2111466366 · doi:10.1002/jbmr.2227

Reduction in Proximal Femoral Strength in Patients With Acute Spinal Cord Injury

2014· article· en· W2111466366 on OpenAlexaff
W. Brent Edwards, Thomas J. Schnitzer, Karen L. Troy

Bibliographic record

VenueJournal of Bone and Mineral Research · 2014
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsTransCanada (Canada)University of Calgary
Fundersnot available
KeywordsMedicineBone mineralSpinal cord injuryFemoral neckFemurQuantitative computed tomographyReduction (mathematics)Dual energyFemoral fractureSurgeryBone densityOsteoporosisNuclear medicineSpinal cordInternal medicine

Abstract

fetched live from OpenAlex

Bone loss after spinal cord injury (SCI) is associated with an increased risk of fracture resulting from minor trauma. Proximal femoral fractures account for approximately 10% to 20% of the fractures in this population and are among the most serious of injuries. Our purpose was to quantify changes to proximal femoral strength in patients with acute SCI. Thirteen subjects received dual-energy X-ray absorptiometry (DXA) and clinical computed tomography (CT) scans at serial time points during acute SCI separated by a mean of 3.5 months (range 2.6 to 4.8 months). Areal bone mineral density (aBMD) at the proximal femur was quantified from DXA, and proximal femoral strength was predicted using CT-based finite element (FE) modeling in a sideways fall configuration. During the acute period of SCI, femoral neck and total proximal femur aBMD decreased by 2.0 ± 1.1%/month (p < 0.001) and 2.2 ± 0.7%/month (p < 0.001), respectively. The observed reductions in aBMD were associated with a 6.9 ± 2.0%/month (p < 0.001) reduction in femoral strength. Thus, changes in femoral strength were some 3 times greater than the observed changes in aBMD (p < 0.001). It was interesting to note that in just 3.5 months of acute SCI, reductions in strength for some patients were on the order of that predicted for lifetime declines owing to aging. Therefore, it is important that therapeutic interventions are implemented soon after SCI in an effort to halt bone loss and decrease fracture risk. In addition, clinicians utilizing DXA to monitor bone health after SCI should be aware of the potential discrepancy between changes in aBMD and strength.

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)0.000
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 score0.181
Threshold uncertainty score0.239

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.030
GPT teacher head0.358
Teacher spread0.327 · 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

Citations45
Published2014
Admission routes1
Has abstractyes

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