Improved Evaluation of Postoperative Pain After Photorefractive Keratectomy
Bibliographic record
Abstract
PURPOSE: Postoperative pain remains an important limiting factor to the selection of photorefractive keratectomy (PRK). There is a consensus in neurology pain research that pain should be evaluated as a multidimensional concept, which differs from current practice in ophthalmology. The purpose of this paper was to validate the use of multidimensional questionnaires, such as the Brief Pain Inventory (BPI) and the McGill Pain Questionnaire (MPQ), to provide an improved analysis of pain after PRK and to better describe its temporal profile. METHODS: This prospective study included 43 eyes of 43 myopic patients who underwent unilateral PRK. After surgery, usual pain treatment was administered. All of the participants responded to the Visual Analogue Scale (VAS), the BPI and the MPQ 1, 24, 48, 72, and 96 hours after surgery. The internal consistency was evaluated, different postoperative periods were compared, and convergent validity was assessed using correlation testing. RESULTS: The Cronbach alpha test showed high internal consistency for each of the questionnaire subscales. Patients reported higher postoperative pain values at the first measurement of the VAS (4.93 ± 2.38), MPQ-pain rating index (26.95 ± 10.58), BPI-pain severity index (14.53 ± 7.36), and BPI-pain interference index (22.30 ± 15.13). Almost all of the scales and subscales showed a statistically significant direct correlation with the VAS at all of the evaluation periods. CONCLUSIONS: This study validated the utility of multidimensional questionnaires to expand the assessment of the PRK postoperative pain profile, including intensity and other qualitative aspects.
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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 | 0.002 | 0.004 |
| 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) | 0.001 | 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".