Polycaprolactone versus collagen membrane and 1‐year clinical outcomes: A randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Polycaprolactone (PCL) is a synthetic aliphatic polyester widely used in biomedical applications with biodegradability in the body and promotes cell proliferation and differentiation. A newly developed bilayered PCL membrane was developed for possibly being used as a membrane in guided bone regeneration (GBR). PURPOSE: To compare the clinical efficacy between a newly developed bilayered PCL membrane with a Cytoplast™ RTM collagen membrane for GBR with simultaneous implant placement. MATERIALS AND METHODS: Twenty-four patients were randomized to PCL or RTM group, and a total of 24 dental implants were placed. Primary outcomes were patient mean buccal bone thickness (BBT) immediately postimplantation and at 6 months using cone-beam CT and soft tissue surface dimensional changes (STC) at crown insertion, 6 months, and 1 year after loading using intraoral scanner. Secondary outcomes included success rate, clinical parameters, healing index, implant stability, pink esthetic score, and marginal bone levels. RESULTS: The percentage of reduced BBT at 6 months was 32.38%, 25.94%, and 23.96% in the test group and 34.42%, 14.75%, and 6.34% in the control group at the corresponding levels. The mean difference of changed BBT associated with PCL membrane, when compared to collagen membrane, at 6 months was -0.02 ± 0.18 mm (95% confidence interval [CI]: -0.40 to 0.35), 0.29 ± 0.28 mm (95% CI: -0.29 to 0.87), and 0.62 ± 0.38 mm (95% CI: -0.17 to 1.42) at 0, 2, and 4 mm from implant shoulder. Minimal loss of STC was observed in both groups up to 1 year of loading. The mean difference loss of surface dimensional change associated with PCL membrane, when compared to collagen membrane, at 1 year of loading was 0.31 ± 0.19 mm (95% CI: -0.07 to 0.70), 0.22 ± 0.26 mm (95% CI: -0.33 to 0.76), and 0.17 ± 0.30 mm (95% CI: -0.45 to 0.78) at 0, 2, and 4 mm from implant shoulder. None of these differences were statistically significant (unpaired t-test, degrees of freedom [df] = 22; p > 0.05). CONCLUSION: Within the limits of this trial, both barrier membranes resulted in comparable outcomes for GBR with implant placement after 1 year in function. Further research is necessary with a larger sample size.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".