Development and standardisation of ‘time-in-range’ measurement for clinical endpoints in retinal diseases
Bibliographic record
Abstract
Clear and updated endpoints are required to measure outcomes of a disease course and/or a therapeutic intervention. The aim of this review is to identify a reliable 'time-in-range' endpoint of clinical outcomes in ocular conditions, with a particular focus on exudative diseases involving the posterior pole of the eye, and to explore possible applications of this endpoint. A PubMed search was carried out pertaining to: 'time-in-range', 'clinical-outcome', 'clinical-endpoint', 'clinical trial', 'metrics', 'retina', 'retinopathy', 'macular-oedema', 'maculopathy', 'ophthalmology', 'visual-function', 'visual acuity end-point' and 'OCT'. The results showed that both functional and morphological endpoints have been used in the evaluation of retinal diseases. At present, the most widely accepted and clinically meaningful marker of ocular disease is 'mean change' in best corrected visual acuity (BCVA). While comparisons to baseline at various timepoints are commonly recommended to evaluate statistical and clinically relevant differences, few metrics capture the disease course continuously over time. In other medical fields, 'time-in-range' has been introduced to provide more complete information on the fluctuations characterising the course of a disease. The application of 'time-in-range' on BCVA in exudative diseases involving the posterior pole seems feasible, reliable and applicable in clinical practice. BCVA 'time-in-range' offers a useful and practical endpoint in retinal diseases, evaluating both visual function at the end of an observation/treatment and fluctuations in disease over time. It may also be applied to other clinical and morphological endpoints in ocular diseases, including macular thickness. This review presents a hypothesis-generating framework proposing 'time-in-range' as a supplementary metric, pending prospective validation.
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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 | 0.132 | 0.217 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".