P.148 Consult and kyphoplasty delay impacts on geriatric vertebral compression fracture outcomes
Bibliographic record
Abstract
Background: Vertebral compression fractures (VCF) lead to both considerable morbidity and increased mortality. Kyphoplasty, a minimally invasive surgery, treats VCFs providing significant pain relief, preserving vertebral height, and reducing spinal deformity. Methods: A retrospective cohort study at Hamilton Health Sciences (HHS) was conducted on elderly patients (60 years or older) who underwent kyphoplasty at between 2012 and 2022. The patients had prior hospital admissions under non-spine-related specialties at HHS within two years before their surgery. Primary outcomes were the progression of vertebral height loss and focal kyphotic deformity. Results: The study included 119 patients (52.1% female, mean age 70.71 years). A significant increase in vertebral height loss was observed from diagnosis to pre-kyphoplasty (0.32% change, p < 0.0001) and from diagnosis to post-kyphoplasty (0.24% change, p = 0.015). However, there were no significant correlations between delay times and changes in vertebral height or focal kyphotic deformity. Conclusions: Delays in neurosurgical consultation and kyphoplasty did not significantly affect radiographic outcomes in elderly patients with VCF despite the progression of vertebral height loss. This suggests that while timely patient care is essential, delayed treatment may not adversely affect key radiographic metrics in elderly VCF patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".