Prevalence of phantom eye syndrome following eye removal: a systematic review and meta-analysis
Bibliographic record
Abstract
Purpose To determine the literature-pooled prevalence of phantom eye syndrome (PES) following eye removal, including phantom vision (PV), phantom pain (PP), or phantom non-visual non-painful (PNVNP) sensations.Methods Databases were searched from inception to March 12 2025. A systematic review and meta-analysis of PES prevalence and risk factors was conducted.Results Seven studies were identified (775 patients). The literature-pooled prevalence of PES, defined as having at least one constitutive symptom, was 58.9% (95% CI [50.4, 66.9], I2 = 78%, five studies). The most common constitutive symptom was PV in 35.6% (95% CI [29.5, 42.3], I2 = 71.1%, six studies), followed by PP in 26.4% (95% CI [20.8, 32.9], I2 = 76.1%, seven studies) and PNVNP in 19.9% (95% CI [7.9, 42.0], I2 = 91.2%, six studies) sensations. The pooled prevalence of reporting all three constitutive symptoms simultaneously was 4.8% (95% CI [2.2, 10.1], I2 = 67.3%, three studies). Commonly reported risk factors in the literature included mental health comorbidities and preoperative pain, though some studies did not conduct multivariable analysis to control for confounding factors.Conclusions Low certainty evidence supports that over 50% of patients may develop at least one constitutive PES symptom. Patients may benefit from PES screening, reassurance, and early treatment of postoperative pain.
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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 | 0.014 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.036 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".