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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".