Glaucoma screening: analysis of conventional and telemedicine‐friendly devices
Bibliographic record
Abstract
PURPOSE: Portable, telemedicine-friendly devices offer novel opportunity for screening and monitoring glaucoma in the remote and rural regions of the world. This study examines the effective combination of telemedicine-friendly screening devices for detection of glaucoma in relation with conventional, hospital-based devices. METHODS: A total of 399 eyes were screened with telemedicine-friendly devices and conventional, hospital-based devices such as ophthalmoscope, tonometer and perimeter. RESULTS: Combination of age and family history of glaucoma alone has a sensitivity of 35.6% (specificity 94.2%, area under the curve 0.81, correctly classified 81.1%) and an addition of telemedicine-friendly or conventional visual field tests optimized the sensitivity to 91.1% (specificity 93.6%, area under the curve 0.95, correctly classified 93%). Analysis indicates good agreement between vertical cup-to-disc ratio by ophthalmoscopy and digital image reading. An addition of intraocular pressure test does not change sensitivity (35.6%) and specificity (94.2%). CONCLUSION: This study indicates that evaluations of cup-to-disc ratio and visual field, using telemedicine-friendly devices, are most useful tools in screening for glaucoma. When used together these devices may be an alternative for conventional glaucoma screenings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".