Porous and nonporous orbital implants for treating the anophthalmic socket: A meta-analysis of case series studies
Bibliographic record
Abstract
PURPOSE: To assess the efficacy and safety of porous and nonporous implants for management of the anophthalmic socket. METHODS: Case series meta-analysis was conducted with no language restriction, including studies from: PUBMED, EMBASE and LILACS. Study eligibility criteria were case series design with more than 20 cases reported, use of porous and/or nonporous orbital implants, anophthalmic socket and, treatment success defined as no implant exposure or extrusion. Complications rates from each included study were quantified. Proportional meta-analysis was performed on both outcomes with a random-effects model and the 95% confidential intervals were calculated. RESULTS: A total of 35 case series studies with a total of 3,805 patients were included in the meta-analysis. There are no studies comparing porous and nonporous implants in the anophthalmic socket treatment. There was no statistically significant difference between porous polyethylene (PP) and hydroxyapatite (HA) on implant exposure: 0.026 (0.012-0.045) vs 0.054 (0.041-0.070), respectively and, neither on implant extrusion: 0.0042 (0.0008-0.010) vs. 0.018 (0.004-0.042), respectively. However, there was a significant difference supporting the use of PP when compared to bioceramic implant: 0.026 (0.012 -0.045) vs. 0.12 (0.06-0.20), respectively, on implant exposure. CONCLUSION: PP implants showed lower chance of exposure than bioceramic implant for anophthalmic socket reconstruction, although we cannot rule out the possibility of heterogeneity bias due to the nature and level of evidence of the included studies. Clinical trials are necessary to expand the knowledge of porous and nonporous orbital implants in the anophthalmic socket management.
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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 | 0.020 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.049 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".