Preoperative Predictors for Acute Pain After Photorefractive Keratectomy
Bibliographic record
Abstract
PURPOSE: The aim of this study was to identify preoperative predictors for the occurrence of early severe postoperative pain in patients undergoing photorefractive keratectomy (PRK). The implementation of preoperative screening methods may facilitate more specific or aggressive pain therapies specifically targeted to individuals at a high risk of experiencing severe postoperative pain. METHODS: This was exploratory research that included patients who underwent PRK. Before PRK, patients were administered a sociodemographic questionnaire, the Pain Catastrophizing Scale, and the State-Trait Anxiety Inventory and underwent corneal sensitivity and conditioned pain modulation (CPM) tests. Post-PRK pain was assessed using a pain intensity visual analog scale (VAS), and the short-form McGill Pain Questionnaire (SF-MPQ) was completed 21 days before PRK and 1, 24, 48, and 72 hours after PRK. Spearman correlations were calculated for pain scores and preoperative predictors. RESULTS: This research included 34 eyes of 34 patients. Preoperative corneal sensitivity was positively correlated with post-PRK pain scores as assessed by VAS and SF-MPQ (rho = 0.39 and rho = 0.41, respectively, P < 0.05). No correlations were found between Pain Catastrophizing Scale, State-Trait Anxiety Inventory, and CPM scores and post-PRK pain scores ( P > 0.05). CONCLUSIONS: Abnormal presurgical corneal sensitivity was a protective marker for severe pain after PRK, while scores as assessed by VAS and SF-MPQ and CPM were not related to 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 | 0.000 | 0.003 |
| 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".