Building a Stronger Backbone: 3D Printing's Role in Treating Spinal Cord Conditions
Bibliographic record
Abstract
Spinal cord injuries (SCIs) pose significant challenges as complete nerve regeneration remains limited. The demand for improved technologies in SCI treatment is evident. One such emerging technology is three-dimensional printing (3DP), which, coupled with advancements in medical imaging and bioengineering, has significantly enhanced precision in surgical procedures. This systematic review aims to explore 3DP as a treatment option for SCIs, examining its cost, efficacy, safety, and the associated technological constraints. A systematic search of Medline was conducted through PubMed for literature published since 2019. The search results were exported to Rayyan for abstract and full-text screening following predefined criteria. The risk of bias in the selected studies was assessed using the RoB2 tool and the Newcastle-Ottawa Scale. From a total of 89 articles screened, 11 studies met the eligibility criteria, collectively assessing 237 individuals with various types of SCIs, including lumbar degeneration, en bloc resection of thoracolumbar metastasis, adult spinal deformity, and cervical degeneration. These studies examined the utilization of 3DP devices such as hand orthosis, interbody fusion cages, lamellar titanium cages, artificial vertebral bodies, and others. Most of the reviewed studies reported positive treatment outcomes, with the actual procedure costs varying from $65 to $5,000. Recent literature shows positive outcomes in the use of 3DP technologies for SCIs, highlighting its potential for enhancing both surgical and nonsurgical interventions. These advancements usher in a new era in SCI treatment, providing enhanced precision and a wider range of treatment options, ultimately leading to more comprehensive and effective patient care.
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 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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".