The efficacy of Artificial Intelligence to predict Post-operative Outcomes in Posterior Segment Ophthalmic Surgeries - A systematic review and meta-analysis
Bibliographic record
Abstract
R eview question / Objective To assess the current knowledge regarding the use of artificial intelligence in predicting postoperative outcomes in patients undergoing posterior segment ophthalmic surgery.Condition being studied Population: Patients undergoing ophthalmic surgery on the posterior segment of the eye.Intervention: Utilization of artificial intelligence as a predictive tool.Outcome: Successful prediction of post-operative outcomes.METHODS Search strategy A systematic and thorough database search will include MEDLINE, Embase, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, IEEE, Compendex, Web of Science, Scopus, and ProQuest Dissertations and Theses.Additionally, a grey literature search will be performed using Google Scholar.Participant or population Population: Patients undergoing ophthalmic surgery on the posterior segment of the eye.Intervention Intervention: Utilization of artificial intelligence as a predictive tool.Comparator Not utilizing AI to predict postoperative outcome of posterior segment ophthalmic surgeries.Study designs to be included Inclusion of artificial intelligence for predicting patient outcomes after ophthalmic surgical procedures in the posterior segment of the eye.Studies on laserbased procedures without a surgical element and other non-surgical procedures will not be included.Studies that did not define and predict at least one patient outcome will also be excluded from the review.No restrictions on the types of studies.Eligibility criteria Inclusion criteria: Primary studies using AI to predict outcomes after ophthalmic surgical procedures.Exclusion criteria
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.012 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".