Prevalence of systemic disease in patients undergoing cataract surgery at a hospital versus outpatient clinic
Bibliographic record
Abstract
OBJECTIVE: To compare demographic and clinical characteristics of patients undergoing cataract surgery at a hospital versus a private outpatient clinic (POC) within a shared funding model in Ontario, Canada. Our tertiary academic hospital operates a unique funding arrangement, in which some hospital funds support cataract surgeries at a POC, enabling oversight of provincially funded surgeries at both sites. DESIGN: Retrospective cohort study. PARTICIPANTS: All patients who underwent cataract surgery in 2023 at both the hospital (Kingston Health Sciences Centre) and the affiliated POC were included. METHODS: We compared demographic and clinical data, including age, gender, blood pressure, comorbidities (e.g., diabetes, hypertension), American Society of Anesthesiologists scores, and medication usage. RESULTS: Patients included in this study numbered 3441 (1982 hospital patients and 1459 POC patients). Mean age was similar, although significantly more females underwent surgery at the POC (p = 0.004). Hospital patients had higher systolic blood pressure (p < 0.0001), higher rates of diabetes (1.5 odds ratio, 95% confidence interval [CI] 1.3-1.8; p < 0.0001), higher American Society of Anesthesiologist scores (0.3 mean difference, 95% CI 0.27-0.35; p < 0.0001), and higher smoking rates (p < 0.0001). The use of anticoagulation, antidiabetes, and prostate medications were significantly higher among hospital patients (p < 0.001), with greater use of antihypertensive medications (p = 0.018). CONCLUSIONS: Our data support concerns that healthier patients are more likely to undergo surgery at outpatient clinics. Factors, such as mandated restrictions on patient selection at POCs, surgeon preference, and referral patterns may contribute. As outpatient surgical care grows, documenting these differences is essential to ensure fair distribution of resources and equitable access to care.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".