Dry eye disease: A Canadian quality of life and productivity loss survey
Bibliographic record
Abstract
ABSTRACT Aim To capture the direct and indirect cost estimates of dry eye disease (DED), stratified by disease severity, in patients from Canada and to understand the impact of DED on quality of life (QoL) in this group. Methods A prospective, multi-centre, observational, cross-sectional study was conducted at six optometry and ophthalmology sites across Canada. Eligible patients completed a 20-minute survey on demography, general health, disease severity, QoL, and direct and indirect costs. Results A total of 151 patients participated in the study and 146 were included in the analysis. Mean (standard deviation [SD]) age was 49.8 (11.4) years and most patients were female (89.7%). DED was considered moderate or severe by 19.2% and 69.2% of patients, respectively. Sjögren’s syndrome was reported by 8.2% of patients. Total mean annual costs of DED were $24,331 (Canadian dollars [CAD]) per patient and increased with disease severity. Mean (SD) indirect costs for mild, moderate, and severe disease were $5,961 ($6,275), $16,525 ($11,607), and $25,485 ($22,879), respectively. Mean (SD) direct costs were $958 ($1,216), $1,303 ($1,574), and $2,766 ($7,161), respectively. QoL scores were lowest in patients with Sjögren’s syndrome and those with severe DED. Conclusions This study provides important insights into the negative impact of DED in a Canadian setting. Patients with severe DED reported higher direct and indirect costs and lower QoL compared with those with mild or moderate disease. Increased costs and poorer QoL were also evident for patients with DED plus Sjögren’s syndrome versus DED alone.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".