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Record W2785210159 · doi:10.14740/jocmr3287w

Evaluation of Risk Factors for Second Hip Fractures in Elderly Patients

2018· article· en· W2785210159 on OpenAlexvenueno aff
Sabri Batın, Fırat Ozan, Kaan Gürbüz, Şemmi Koyuncu, Fatih Vatansever, Erdal Uzun

Bibliographic record

VenueJournal of Clinical Medicine Research · 2018
Typearticle
Languageen
FieldMedicine
TopicHip and Femur Fractures
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHip fractureOsteoporosisSurgeryPhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Hip fracture is a worldwide public health problem that primarily affects osteoporotic individuals and the elderly. A second hip fracture can occur in elderly patients who have already suffered an initial hip fracture. The aim of this study was to investigate possible risk factors for second hip fractures in elderly patients with hip fractures. METHODS: Between 2010 and 2014, 230 patients who underwent uncemented bipolar hemiarthroplasty for hip fractures were retrospectively analyzed. The patients were divided into two groups: those with a first hip fracture (group 1) and those with a second hip fracture (group 2). RESULTS: The mean time from the first hip fracture to second hip fracture was 22 months. There were no significant differences in the American Society of Anesthesiologist scores, comorbidities were observed in the two groups. The mean length of hospitalization was not significantly different between the two groups. The mean postoperative functional scores after second hip fractures were significantly lower in group 2 than in group 1. CONCLUSIONS: Although there are not certain risk factors for second hip fractures in elderly patients with hip fractures, to prevent second hip fractures, elderly patients should be provided with physical and medical therapy as well as orthotic support and their functional activity should be maintained.

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)0.036
metaresearch head score (Gemma)0.101
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.274
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0360.101
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.344
GPT teacher head0.608
Teacher spread0.263 · 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; both teacher heads agree on what is shown here.

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

Citations13
Published2018
Admission routes1
Has abstractyes

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