Contemporary Management of Diabetic Retinopathy in Canada: From Guidelines to Algorithm Guidance
Bibliographic record
Abstract
Recent advances in the therapeutic options and approaches for diabetic retinopathy (DR) and diabetic macular edema (DME) have resulted in improved visual outcomes for many patients with diabetes. Yet, they have also created many clinical dilemmas for treating ophthalmologists and retina specialists, including treatment selection, initiation, frequency and duration. With this in mind, a panel of Canadian retina specialists met and discussed the current clinical evidence as well as specific situations and scenarios commonly encountered in daily practice. They also shared their experiences and therapeutic approaches. This document, containing a consensus on treatment algorithms for various clinical scenarios, is the result of their lengthy and in-depth discussions and considerations. The intent is to provide a step-by-step approach to the treatment of DR and DME. Although clinicians are encouraged to use and refer to these algorithms as a guide for various situations, they are not meant to be a replacement for sound clinical judgment.
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 | 0.018 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".