Changes in measured intraocular pressure after hyperopic photorefractive keratectomy
Bibliographic record
Abstract
PURPOSE: To investigate the effect of hyperopic photorefractive keratectomy (PRK) on intraocular pressure (IOP) measurements. SETTING: University of Ottawa Eye Institute, Ottawa Hospital, Ottawa, Canada. METHODS: In this retrospective cohort study, IOP and central corneal thickness (CCT) were measured preoperatively and at 1, 2, 3, 6, 12, 18, and 24 months in 191 eyes that had hyperopic PRK with the VISX Star excimer laser. All corrections applied were between +1.00 and +6.50 diopters (D) of sphere and less than 3.75 D of cylinder. RESULTS: At all postoperative examinations, the mean IOP in the hyperopic PRK group was 1.0 to 1.8 mm Hg lower than the preoperative IOP (P <.001). A large range of IOP changes was found across the population; eg, at 6 months, 49% of the eyes had a change in IOP from baseline of at least +/-3 mm Hg. A mean reduction of 19 microm of CCT was found with pachymetry after surgery (P < .001). The change in IOP readings postoperatively was not correlated with age, sex, keratometric readings, or applied correction. Changes in IOP were strongly correlated with preoperative IOP at all time points and with preoperative CCT at 18 and 24 months (P < .001). After hyperopic PRK, the measured IOP was more likely to increase in patients with preoperative IOPs less than 14.5 mm Hg and more likely to decrease in patients with preoperative IOPs above 14.5 mm Hg. CONCLUSION: Changes in IOP after hyperopic PRK were similar to changes after myopic PRK, despite only minimal changes in the CCT. This suggests that hyperopic PRK results in biomechanical effects that modify the elastic properties of the cornea beyond the changes in rigidity expected from central corneal thinning. There was a strong negative correlation between the measured preoperative IOP and the change in IOP postoperatively that was likely the result of regression of the mean effect.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".