The Bioceramic Implant: Evaluation of Implant Exposures in 419 Implants
Bibliographic record
Abstract
PURPOSE: To compare the rate of exposure in the immediate 3-month postoperative follow-up period with the rate of exposure after the immediate postoperative period in 419 anophthalmic patients with a bioceramic (aluminum oxide) orbital implant. METHODS: This is a retrospective, clinical case series of 419 patients who received a bioceramic orbital implant. All patients who presented to five oculofacial surgeons (D.J., S.G., J.D., S.K., L.M.) from January 1, 2000, to June 1, 2007, who received a bioceramic orbital implant and had a minimum of 3 months of follow-up were included in this study. The authors analyzed age, gender, type of surgery, implant size, peg system, follow-up duration, time of pegging, and problems encountered. The data from the patients with greater than 3 months of follow-up with exposure of the bioceramic implant are detailed in this report. RESULTS: There were 353 patients followed for 3 to 96 months with an average of 30 months of follow-up (median 23 months). Implant exposure occurred in 32/353 bioceramic implants (9.1%). Six of the 32 (19%) exposures occurred during the 90-day postoperative period (average 2.1 months). Twenty-six (81%) exposures occurred outside of the 90-day postoperative period (average 27.5 months, range 4-82 months). CONCLUSIONS: Implant exposures can occur anytime postimplant placement. This review discovered an implant exposure rate of 9.1%, with the majority of the exposures occurring after the postoperative follow-up period. Patients with porous orbital implants should be followed on a long-term basis to detect this complication.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".