Headache Attributed to Refractive Error: Improvement after Topography-Guided Photorefractive Keratectomy with Corneal Cross-Linking in Patients with Keratoconus
Bibliographic record
Abstract
Purpose: To investigate if topography-guided photorefractive keratectomy (TGPRK) alleviates headache, particularly headache attributed to refractive errors (HARE) in keratoconus. Methods: Patients diagnosed with keratoconus undergoing TGPRK for refractive correction were included. Best spectacle corrected visual acuity (BSCVA) using the logMAR scale and refractive error were measured. Patients answered a questionnaire exploring headaches, characteristics, treatment, and the Headache Impact Test (HIT-6) before and 6 months after the surgery. Results: 40 patients were included. Preoperatively, 24 patients (60%) met criteria for headaches: five for migraine, 14 for HARE, and five for tension-type headache (TTH). Patients with headaches preoperatively were more likely to require bilateral TGPRK, and the mean sphere and cylindrical power were higher. Postoperatively, 15 out of the 24 patients of the headache group experienced complete resolution of headaches, and only nine patients met diagnostic criteria for headaches: two for migraine, six for HARE, and one for TTH. The number of headaches reduced from 4.4 ± 2.4 to 0.5 ± 0.7 days/week (p < 0.001). Headache duration decreased from 108.5 ± 100.7 min to 34.4 ± 63.5 min (p = 0.002). Postoperatively, the consumption of analgesia decreased. The HIT-6 revealed an improvement in the quality-of-life post-procedure (p < 0.001). Conclusions: Surgical correction of irregular astigmatism in patients with keratoconus can alleviate or resolve headaches in a large proportion of patients, resulting in an improvement in their quality of life. Physicians should consider keratoconus in patients fitting criteria for HARE not alleviated by spectacle correction and suboptimal vision in glasses.
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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 | 0.001 |
| 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".