Worldwide Argus II implantation: recommendations to optimize patient outcomes
Bibliographic record
Abstract
BACKGROUND: A position paper based on the collective experiences of Argus II Retinal Prosthesis System investigators to review strategies to optimize outcomes in patients with retinitis pigmentosa undergoing retinal prosthesis implantation. METHODS: Retinal surgeons, device programmers, and rehabilitation specialists from Europe, Canada, Middle East, and the United States were convened to the first international Argus II Investigator Meeting held in Ann Arbor, MI in March 2015. The recommendations from the collective experiences were collected. Factors associated with successful outcomes were determined. RESULTS: Factors leading to successful outcomes begin with appropriate patient selection, expectation counseling, and preoperative retinal assessment. Challenges to surgical implantation include presence of staphyloma and inadequate Tenon's capsule or conjunctiva. Modified surgical technique may reduce risks of complications such as hypotony and conjunctival erosion. Rehabilitation efforts and correlation with validated outcome measures following implantation are critical. CONCLUSIONS: Bringing together Argus II investigators allowed the identification of strategies to optimize patient outcomes. Establishing an on-line collaborative network will foster coordinated research efforts to advance outcome assessment and rehabilitation 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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".