A national survey of Canadian ophthalmologists to determine awareness of published guidelines for the management of uveitis
Bibliographic record
Abstract
BACKGROUND: The objectives of this study are to assess Canadian ophthalmologists' awareness of established uveitis treatment guidelines and clinical management of uveitis and to assess the frequency of government applications for immunomodulatory therapy (IMT) and identify primary prescribers. A 25-item questionnaire was sent to 759 practicing Canadian ophthalmologists. Six questions assessed demographics including the year of residency completion, training by uveitis specialists during residency, and fellowship training. Five questions assessed application of guidelines to clinical scenarios, and 12 questions assessed referral patterns and success of obtaining coverage for IMT. RESULTS: Of 144 respondents, 12 (8.3 %) were uveitis specialists; 45.1 % of respondents had uveitis training during residency by a uveitis specialist. Sixty-one percent reported awareness of management guidelines. Recent graduates (2001-2012) referred patients to uveitis specialists (55.3 %) less frequently than earlier graduates. Recent graduates also managed uveitis patients more frequently with corticosteroid injections (15.6 %) than those who graduated before 1980 (9.75 %). The majority (93.6 %) of respondents submitted less than six IMT funding applications for provincial drug coverage yearly, and 5.5 % reported prescribing IMT themselves, rather than referring to other specialists. CONCLUSIONS: Although greater than half of respondents reported awareness of uveitis treatment guidelines, Canadian ophthalmologists' awareness of uveitis treatment guidelines and application of the guidelines to patient care could be improved. Few applications are made for IMT, and the majority of applications are sent by non-ophthalmologists. This suggests the need for further education of ophthalmologist about uveitis treatment guidelines and for more ophthalmologists trained to manage uveitis with IMT.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".