Exploring the Relationship Between the Social Determinants of Health and Health Inequities in Pediatric Ophthalmology
Bibliographic record
Abstract
Background: There is limited evidence on the impact of social determinants of health (SDH) on pediatric ophthalmology outcomes, specifically Rare Pediatric Eye Cancers (R-PECs) in Canada. Objective: To characterize the availability of SDH data in the electronic health record (EHR) of pediatric ophthalmology patients and to examine the association of SDH with attendance at medical visits. We also examined the association of R-PEC patient SDH with (i) medical visit attendance, (ii) emergency visits, (iii) care plan delay, (iv) age and stage at diagnosis, and (v) clinical outcomes. Methods: This retrospective cohort study between 1-June-2018 and 6-October-2023 included pediatric ophthalmology patients managed at The Hospital for Sick Children. Pearson Chi-squared analysis and multivariable and binomial regression with adjusted odds ratios (aOR) and 95% confidence intervals (CI) were performed (significance was set at p<0.05). Results: Coverage of SDH within EHRs was highly variable and there was a significant quantity of missing data. Our findings suggest that SDH can influence medical visit attendance, disease classification, and clinical outcomes. Conclusion: Addressing unfavorable SDH could serve to improve medical visit attendance, age and stage at diagnosis, final visual outcome and reduce, emergency room visits and delay of care among pediatric ophthalmology patients.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".