Rebound Self‐tonometry Acquisition Time and Ease of Use Evaluated by Newly Trained Optometry Students and Optometrists
Bibliographic record
Abstract
SIGNIFICANCE: Peak IOP and IOP fluctuations have been implicated as risk factors for glaucoma progression. Peak 24-hour IOP can be significantly higher than in-office measurements. Icare HOME could be a useful adjunct in glaucoma management if positively appraised by individuals familiar with eye care. PURPOSE: The purpose of this study was to measure the time needed for a nonclinical convenience sample of optometry students and optometrists to self-measure IOP using Icare HOME and to determine their perceptions of rebound self-tonometry. METHODS: A total of 234 subjects were enrolled, with 226 (97%) having a complete data set. Self-measurement was performed on the study eye using Icare HOME while seated and without contact lenses. Examiners self-measured IOP while subjects observed; examiners then measured subjects' IOP. Subjects then completed self-measurement while timed. Only one attempt was allowed. Time and study eye were recorded, and subjects completed a short survey. Descriptive statistics were conducted. RESULTS: Mean ± standard deviation age was 34.6 ± 13.3 years (58.3% female, 52.3% contact lens wearers). Test time ranged from 3 to 366 seconds, with 38% able to self-measure in 10 seconds or less, 74% in 60 seconds or less, and 92.8% in 120 seconds or less; 5.8% were unable to self-measure IOP. There was no significant correlation between test time and age (r = -0.03, P = .67). The device was reported to be easy or very easy to use by 69.7% of subjects and comfortable or very comfortable by 90.4% of subjects. After the study, 89.1% of subjects perceived that rebound self-tonometry has a role in the management of patients with glaucoma and suspicion of glaucoma. CONCLUSIONS: The majority of neophyte subjects perceived self-measurement of IOP as having a role in the management of glaucoma and suspicion of glaucoma. They rated Icare HOME as comfortable and easy to use and were able to self-measure IOP on the first attempt.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".