Bibliographic record
Abstract
Clinically, low vision (LV) is defined as an untreatable impairment that limits perti-nent activities of daily living (ADL), and is one of the 10 most prevalent causes of disability.1 Given current demographic changes, most cases of LV are caused by age-related eye diseases,2 particularly, age-related macular degeneration (AMD). AMD is the leading cause of blindness in Canada and the United States (US) for people>65 years old and the second leading cause for those between the ages of 45 and 65 years.3 Despite past and recent advances in treatment for ocular diseases, many remain incurable and result in LV. Continuation of care mandates vision rehabilitation inter-vention as the only remaining option for such patients. LV rehabilitation (LVR) is a relatively new subspecialty in eye care that can assist LV patients with technology and techniques designed to enhance residual abilities required to perform vision-dependent tasks in a useful manner. This issue of Ophthalmology Rounds reviews recent advances in LVR. It summarizes the components of LVR and their applica-tions, and discusses the assessment of residual functional vision such as reading
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.733 | 0.421 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".