Application of deformity principles in the management of spinal neoplasms: A Primer
Bibliographic record
Abstract
Background: With advances in surgical techniques, radiation, and systemic therapy, prognoses and quality of life have improved amongst patients with primary and metastatic vertebral column tumors. Sagittal deformity is known to have an adverse impact on patient quality of life but has been largely ignored in this study population. Methods: A comprehensive literature review was conducted, focusing on articles germane to the study of spinal deformity in the context of oncologic disease. Articles included those focusing on bone health, the association of spinal deformity with oncologic spine disease, and both pelvic and anterior column reconstruction in patients treated for primary tumors. Results: Little to date has focused specifically on the management of spinal deformity in the context of spinal tumors. However, it is known that tumor involvement of the vertebral column is associated with poorer screw purchase, which can be further worsened by radiotherapy. Instrumentation techniques that seek to address underlying deformity must also balance the need for radiographic follow-up, which is improved with novel carbon fiber-reinforced polyetheretherketone implants, and the need for intraoperative contouring. Last, residual deformity is associated with poorer patient reported outcomes and increased mechanical complications in adult spinal deformity, but better study within the spinal oncology population is merited. Conclusion: The potential negative impact of spinal deformity on patient quality of life in the spinal oncology population is now better appreciated amongst spinal oncologists, but studies have been limited to date. Further investigation is merited as survival outcomes continue to improve.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".