Role of Posterior Carbon Fiber Implants in Spine Tumor Surgery
Bibliographic record
Abstract
STUDY DESIGN: Narrative Review. OBJECTIVE: The management of spinal tumors requires a multi-disciplinary approach including surgery, radiation, and systemic therapy. Surgical approaches typically require posterior segmental instrumentation to maintain long-term spinal stability. Carbon fiber reinforced pedicle screws (CFRP) are increasingly used in the oncologic setting due to reductions in both imaging artifacts and radiotherapy perturbations compared to titanium implants. We performed a review of the literature and highlight advantages and future areas of study for CFRP. METHODS: We performed a systematic review of the literature using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and identified 10 articles including 573 patients. Across all studies we reviewed patient demographics, tumor types treated, hardware-related features, complication rates, recurrence, survival, and follow-up. RESULTS: Across 10 studies, a total of 1371 screws placed. Surgical and non-surgical complications were reported in 18.3% of patients. Disease progression at the surgical site was detected in 7.3% of patients. There was no significant difference in clinical or hardware complications between CFRP or titanium implants. The most frequent complication attributable to implanted CFRP hardware included screw breakage in 2.4% and loosening in 1.7% of patients, respectively. CONCLUSION: CFRP provide a unique tool in the setting of spinal oncology. With a safety profile comparable to titanium, we review the documented advantages of CFRP posterior implants compared to titanium, while also addressing their current limitations. Additionally, we highlight several areas of future research to identify the optimal patients who will achieve the greatest benefit of CFRP.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".