Efficacy of Care for Blind Painful Eyes
Bibliographic record
Abstract
PURPOSE: Pain relief for a blind painful eye often follows an escalating paradigm of interventions. This study compares the efficacy of common interventions. METHODS: A retrospective chart review of blind painful eye cases was conducted at a single tertiary institution from April 2012 to December 2016. Demographics, etiology, treatment, and pain level were assessed. RESULTS: Among 99 blind painful eyes, 96 eyes initially received medical therapy (topical steroids, cycloplegics, and/or hypotensives), with pain relief in 39% of eyes. Minimally invasive interventions (laser cyclophotocoagulation, retrobulbar injection, or corneal electrocautery) were performed 41 times in 36 eyes, 34 of which had failed medical therapy, and led to pain relief in 75% of eyes. Evisceration or enucleation was performed in 28 eyes, and long-term pain relief was achieved in 100% of eyes. Surgery allowed discontinuation of oral analgesics in 100% of cases versus 20% for minimally invasive therapy (p = 0.005) and 14% for medical therapy (p = 0.0001). Compared with medical therapy, minimally invasive therapy was 2.5 times more likely to achieve lasting pain relief (p = 0.003) and surgical therapy 35.6 times more likely to achieve lasting pain relief (p = 0.011). High initial pain score was associated with nonsurgical treatment failure. CONCLUSIONS: Medical therapy provides pain relief in a moderate number of patients with a blind painful eye. When medical therapy fails, minimally invasive therapy and surgical interventions are successively more effective in relieving ocular pain. High initial pain score is a risk factor for nonsurgical therapy failure and may merit an earlier discussion of surgical intervention.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".