Application of 3D‑printed porous titanium interbody fusion cage vs. polyether ether ketone interbody fusion cage in anterior cervical discectomy and fusion: A systematic review and meta‑analysis update
Bibliographic record
Abstract
The present study aimed to compare the differences between 3D-printed porous titanium and polyether ether ketone (PEEK) interbody fusion cages for anterior cervical discectomy and fusion (ACDF). Literature on the application of 3D-printed porous titanium and PEEK interbody fusion cages for ACDF was searched in the PubMed, Web of Science, Embase, China National Knowledge Infrastructure, Wanfang and VIP databases. A total of 1,181 articles were retrieved and 12 were finally included. The Cochrane bias risk assessment criteria and Newcastle-Ottawa scale were used for quality evaluation and Review Manager 5.4 was used for data analysis. The 3D cage group was superior to the PEEK cage group in terms of operative time [mean difference (MD): -7.68; 95% confidence interval (CI): -11.08, -4.29; P<0.00001], intraoperative blood loss (MD: -6.17; 95%CI: -10.56, -1.78; P=0.006), hospitalization time (MD: -0.57; 95%CI: -0.86, -0.28: P=0.0001), postoperative complications [odds ratio (OR): 0.35; 95%CI: 0.15, 0.80; P=0.01], C2-7 Cobb angle (MD: 2.85; 95%CI: 1.45, 4.24; P<0.0001), intervertebral space height (MD: 1.20; 95%CI: 0.54, 1.87; P=0.0004), Japanese Orthopaedic Association Assessment of Treatment (MD: 0.69; 95%CI: 0.24, 1.15; P=0.003) and visual analogue scale score (MD: -0.43; 95%CI: -0.78, -0.07; P=0.02). The difference was statistically significant, while there was no significant difference between the two groups in terms of fusion rate (OR: 1.74; 95%CI: 0.71, 4.27; P=0.23). The use of 3D-printed porous titanium interbody fusion cage in ACDF has the advantages of short operation time, less bleeding loss, shorter hospitalization time and fewer postoperative complications. It can better maintain the cervical curvature and intervertebral height, relieve pain and accelerate postoperative functional recovery.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.024 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".