Bioabsorbable implants are a viable alternative to traditional metallic implants in orthopaedic surgery: a systematic review and meta-analysis
Bibliographic record
Abstract
ABSTRACT: Introduction This systematic review and meta-analysis of randomized controlled trials (RCTs) aims to compare clinical outcomes and complication profiles of bioabsorbable versus metallic implants in orthopaedic surgery. Methods Four databases (PubMed, MEDLINE, EMBASE, CINAHL) were searched from inception to April 8, 2025, for RCTs comparing bioabsorbable and metal implants in orthopaedic procedures. Data were extracted on demographics, implant type, complications, and outcomes. Meta-analyses using random-effects models were performed for pooled comparisons, and heterogeneity was quantified using the I 2 statistic. Risk ratios (RR) and mean differences (MD) were calculated with 95% confidence intervals (CI). Results Twenty-seven RCTs involving 1437 patients (738 bioabsorbable, 699 metallic) were included. Overall complication rates were similar between groups (RR: 1.05, 95% CI: 0.62–1.77, I 2 =49.8%, p=0.85). Bioabsorbable implants had a 15.5% complication rate compared to 13.3% for metallic implants. Newer materials showed lower complication rates of 2.5% for PLLA-HA and 5% for magnesium compared to 16.5% for other compounds. There were no foreign body reactions in the studies using newer materials. There was a statistically significant decrease in surgical site infections in the bioabsorbable group (RR: 0.36, 95% CI: 0.17, 0.79, I 2 =0%, p=0.02). No significant differences were found regarding hardware failure, pain and functional deficiency, and healing rates. Subgroup analysis of ankle procedures (n=5) showed a higher complication rate in the bioabsorbable group (RR: 3.75, 95% CI: 1.02, 13.8, I 2 =25%, p=0.05), influenced by one study reporting a 33.7% foreign body reaction rate. Anterior cruciate ligament reconstruction, foot, and upper limb subgroups showed comparable outcomes. Conclusion Bioabsorbable implants provide comparable complication rates and clinical outcomes to metallic implants across orthopaedic subspecialties. Outcomes may vary by anatomical site and implant generation, with newer bioabsorbable materials demonstrating promising safety profiles. Further long-term studies are warranted to evaluate their performance in high-stress applications and guide clinical decision-making. Level of Evidence Level I – Systematic Review of Randomized Controlled Trials
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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 | 0.017 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.031 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".