Resorbable Material for Pediatric Orbital Floor Reconstruction
Bibliographic record
Abstract
INTRODUCTION: The use of resorbable materials is becoming more popular for pediatric orbital floor reconstruction. The purpose of this systematic review is to evaluate the effectiveness and safety of the various materials used in pediatric orbital floor reconstruction. METHODS: A systematic literature search was performed to identify all relevant articles reporting complications following pediatric orbital floor reconstruction. The search included published articles in three electronic databases-Ovid MEDLINE, EMBASE, and PubMed starting from database establishment to July 2017. Primary endpoints were enophthalmos, diplopia, and infection. Resorbable material was compared to autologous grafts and nonresorbable material. RESULTS: A total of 14 studies containing 248 patients were included in this review. Fifty-four (21.8%) patients had reconstruction performed with autologous grafts, 72 (29.0%) patients with resorbable material, and 122 (49.2%) patients with nonresorbable material. Resorbable materials had the lowest rate of postoperative enophthalmos (3/52; 5.8%) and the highest rate of postoperative diplopia (19/72; 26.4%). In contrast, nonresorbable materials had the lowest rate of postoperative diplopia (5/122; 4.1%), the highest rate of postoperative enophthalmos (14/102; 13.7%). Autologous reconstruction was associated with an 11.1% (4/36) rate of postoperative enophthalmos and a 22.2% (12/54) rate of postoperative diplopia. Nine cases (8.8%) of postoperative infection were documented with nonresorbable materials. No cases of infection were reported with autologous grafts or resorbable materials. CONCLUSION: Newer resorbable implants are safe and have a similar complication profile as traditional autologous grafts in pediatric orbital floor reconstruction.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".