Evaluation of the IOP-Lowering Effect of a Multi-Pressure Dial at Different Negative Pressure Settings
Bibliographic record
Abstract
Purpose: To evaluate the intraocular pressure (IOP)-lowering effect of a multi-pressure dial (MPD) at targeted negative pressure settings. Methods: Prospective, intrasubject controlled study of 65 healthy subjects randomized to receive no negative pressure for 60 minutes or negative pressure application at designated levels of 25%, 50%, and 75% of baseline IOP for 20 minutes each. The main outcome measure was mean IOP with application of negative pressure. Results: In the study eye group, from a baseline IOP of 15.8 ± 3.6 mm Hg, the mean IOP was 13.5 ± 3.4, 11.5 ± 3.1, and 10.2 ± 2.7 mm Hg with negative pressure settings of 25%, 50% and 75%, respectively. In the control eye group, from a baseline IOP of 15.5 ± 3.0 mm Hg, the mean IOP values at the same time points, without negative pressure, were 15.6 ± 3.0, 15.5 ± 2.5 and 15.3 ± 2.4 mm Hg. The difference between the mean IOPs of the two groups was significantly different at all negative pressure settings (P < 0.001) in comparison with baseline. There was one minor adverse event, a corneal abrasion, that was unrelated to device wear. Conclusions: Negative pressure application to the periocular space with a multi-pressure dial can produce titratable IOP reduction while the device is worn with active negative pressure. To our knowledge, this technology represents the first nonpharmacologic, nonlaser, nonsurgical method for IOP reduction. Translational Relevance: This represents the first study demonstrating the IOP-lowering ability of the multi-pressure dial, a device that uses a novel IOP-lowering strategy by delivering negative pressure to the periocular region.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".