Predictors of Early Postoperative Pain After Photorefractive Keratectomy
Bibliographic record
Abstract
PURPOSE: To compare the profiles of postoperative photorefractive keratectomy (PRK) pain between both eyes under the same conditions and to verify the preoperative predictors of pain such as gender, anxiety, knowledge of the procedure, and spherical equivalent refractive error (SERE). METHODS: This prospective study included 86 eyes of 43 patients with myopia who underwent PRK in both eyes at an interval of 14 days between the procedures. Before surgery, subjects answered the State Anxiety Inventory. After surgery, usual PRK pain treatment was given. Subjects answered the Visual Analog Scale, the Brief Pain Inventory (BPI), and the McGill Pain Questionnaire at 1, 24, 48, 72, and 96 hours after surgery. Pain scores and anxiety were compared between each eye using the Wald test and paired Student t test, respectively. The Wald test was performed for gender and SERE for each eye separately. RESULTS: There were no statistically significant differences between both eyes for all time points regarding the Visual Analog Scale, BPI, and McGill Pain Questionnaire-Pain Rating Index pain scores. Subjects were less anxious on average before the second surgery compared with before the first surgery (P < 0.001); however, it was not related to pain ratings after surgery. Gender did not significantly affect any scale of pain, and the SERE between -3 diopters (D) and -5 D (P = 0.035) revealed effects on the BPI. CONCLUSIONS: The profiles of postoperative pain after PRK were similar between both eyes under the same conditions. In this study, a high SERE was the only predictor for increased pain after PRK.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".