Implant extrusion after eye removal for endophthalmitis and panophthalmitis
Bibliographic record
Abstract
PURPOSE: To evaluate the rate of orbital implant extrusion and exposure following enucleation or evisceration in patients with endophthalmitis or panophthalmitis, and to assess the influence of infectious etiology, implant type, and surgical technique on extrusion risk. METHODS: A systematic review was conducted using MEDLINE, CINAHL, Embase, and Scopus for studies published between January 1980 and December 2024. Studies were included if they evaluated implant extrusion or exposure following eye removal surgery in patients diagnosed with endophthalmitis or panophthalmitis. Fourteen retrospective cohort studies met the inclusion criteria. RESULTS: Extrusion or exposure rates ranged from 0% to 53%. Pseudomonas aeruginosa was the most frequently implicated pathogen. Non-porous implants, especially silicone, were more commonly associated with extrusion, while porous implants, particularly hydroxyapatite, demonstrated lower complication rates. No clear difference was observed between evisceration and enucleation in terms of extrusion risk. CONCLUSIONS: Implant extrusion is a significant postoperative complication in the setting or endophthalmitis or panophthalmitis. Pseudomonas aeruginosa and the use of non-porous implants may increase extrusion risk. The use of porous implants and appropriate prophylactic antibiotics may be associated with lower risk of extrusion. Further prospective studies are required to standardize risk assessment and prevention strategies.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".