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Record W2995509033 · doi:10.1097/md.0000000000018217

The effect of bone cement distribution on clinical efficacy after percutaneous kyphoplasty for osteoporotic vertebral compression fractures

2019· article· en· W2995509033 on OpenAlexaff
Shuangjun He, Yijian Zhang, Nanning Lv, Shujin Wang, Yao-Wei Wang, Shuhua Wu, Fan He, Angela Chen, Zhonglai Qian, Jianhong Chen

Bibliographic record

VenueMedicine · 2019
Typearticle
Languageen
FieldMedicine
TopicSpinal Fractures and Fixation Techniques
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMedicineRadiographyBone cementBone mineralPercutaneousCementSurgeryBone densityOsteoporosisDentistryInternal medicine

Abstract

fetched live from OpenAlex

To evaluate the influence of various distributions of bone cement on the clinical efficacy of percutaneous kyphoplasty (PKP) in treating osteoporotic vertebrae compression fractures.A total of 201 OVCF patients (30 males and 171 females) who received PKP treatment in our hospital were enrolled in this study. According to the characteristic of cement distribution, patients were divided into 2 groups: group A ("H" shaped group), the filling pattern in vertebral body were 2 briquettes and connected with / without cement bridge; and group B ("O" shaped group), the filling pattern in vertebral body was a complete crumb and without any separation. Bone mineral density, volume of injected cement, radiographic parameters, and VAS scores were recorded and analyzed between the 2 groups.All patients finished at least a 1-year follow-up and both groups had significant improvement in radiographic parameters and clinical results. No significant differences in BMD, operation time, bleeding volume, or leakage of cement were observed between the 2 groups. Compared with group B, group A had a larger use of bone cement, lower proportion of unipedicular approach, and better VAS scores at 1 year after surgery.Both "H" and "O" shaped distribution pattern can improve radiographic data and clinical outcomes effectively. However, "H" shaped distribution can achieve better clinical recovery at short-term follow-up.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.418
Threshold uncertainty score0.328

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.012
GPT teacher head0.357
Teacher spread0.345 · 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

Citations46
Published2019
Admission routes1
Has abstractyes

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