Attitudes and trends in the use of radiolucent spinal implants: A survey of the North American Spine Society section of spinal oncology
Bibliographic record
Abstract
Background: In spinal oncology, titanium implants pose several challenges including artifact on advanced imaging and therapeutic radiation perturbation. To mitigate these effects, there has been increased interest in radiolucent carbon fiber (CF) and CF-reinforced polyetheretherketone (CFR-PEEK) implants as an alternative for spinal reconstruction. This study surveyed the members of the North American Spine Society (NASS) section of Spinal Oncology to query their perspectives regarding the clinical utility, current practice patterns, and recommended future directions of radiolucent spinal implants. Methods: In February 2021, an anonymous survey was administered to the physicians of the NASS section of Spinal Oncology. Participation in the survey was optional. The survey contained 38 items including demographic questions as well as multiple-choice, yes/no questions, Likert rating scales, and short free-text responses pertaining to the "clinical concept", "efficacy", "problems/complications", "practice pattern", and "future directions" of radiolucent spinal implants. Results: Fifteen responses were received (71.4% response rate). Six of the participants (40%) were neurosurgeons, eight (53.3%) were orthopedic surgeons, and one was a spinal radiation oncologist. Overall, there were mixed opinions among the specialists. While several believed that radiolucent spinal implants provide substantial benefits for the detection of disease recurrence and radiation therapy options, others remained less convinced. Ongoing concerns included high costs, low availability, limited cervical and percutaneous options, and suboptimal screw and rod designs. As such, participants estimated that they currently utilize these implants for 27.3% of anterior and 14.7% of all posterior reconstructions after tumor resection. Conclusion: A survey of the NASS section of Spinal Oncology found a lack of consensus with regards to the imaging and radiation benefits, and several ongoing concerns about currently available options. Therefore, routine utilization of these implants for anterior and posterior spinal reconstructions remains low. Future investigations are warranted to practically validate these devices' theoretical risks and benefits.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".