Methods of Antibiotic Instillation in Porous Orbital Implants
Bibliographic record
Abstract
PURPOSE: Soaking porous implants in antibiotic solution at the time of implantation is often recommended to promote fibrovascular ingrowth and reduce risk of infection. This study evaluated antibiotic penetration in porous implants using different instillation techniques. METHODS: Penetration of methylene blue in three 20-mm diameter porous implants (hydroxyapatite, porous polyethylene, and aluminum oxide) was measured using 4 different techniques: 1) soaking in dye for 5 minutes; 2) compressing the implant in dye in a 60-ml syringe for 1 and 2 minutes; 3) aspirating dye through the implants in a 60-ml syringe for 1 and 2 minutes; and 4) direct injection of dye in the center of the implants. Each implant was cut in half to measure penetration in 4 quadrants by 2 independent observers. RESULTS: Soaking the implants for 5 minutes resulted in 6 mm penetration of dye from the surface in hydroxyapatite and no penetration in the others. Compressing or aspirating implants in dye for both 1 minute and 2 minutes resulted in complete penetration to the center in all implants. Direct injection resulted in complete distribution in hydroxyapatite, localized within 4 mm of the injection site in porous polyethylene, and no penetration in aluminum oxide. CONCLUSIONS: Best penetration of fluid in all implants was achieved with aspiration or compression within a syringe. If an implant becomes infected, topical instillation of antibiotic is unlikely to reach the sites of infected pores. Direct injection of antibiotic may be helpful for porous implants providing the needle pore does not get blocked while penetrating the implant as it did with the 3 aluminum oxide implants tested in this study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".