Social Determinants of Health in Pediatric Ophthalmology Patients: Availability of Data in the Electronic Health Record and Association With Clinic Attendance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose: To characterize the availability of social determinants of health data in the electronic health record of pediatric ophthalmology patients and to examine the association of social determinants of health with attendance at scheduled operating room and clinic visits. Methods: This was a retrospective cohort study of pediatric ophthalmology patients seen at The Hospital for Sick Children between June 1, 2018, and May 23, 2022. Data were collected on demographics, diagnosis, and management-plan. The χ2 tests and multivariable regression were used to examine associations between social determinants of health and attendance at scheduled operating room and clinic visits. Results: The cohort consisted of 26,102 study subjects with 31,288 unique eye-related diagnoses representing 57 unique ICD-10 codes. Availability of data in the electronic health record ranged from 100% for sex, age and postal code to 0.1% for ethnic group. Female sex (P = 0.004) and urbanicity (P = 0.05) were associated with higher operating room visit cancellations. Female sex (P = 0.002), age group 0-13 (P ≤ 0.001), low-medium neighborhood income quintile (P ≤ 0.001), residence of Northern Ontario (P ≤ 0.001), and urbanicity (P ≤ 0.001) were associated with higher clinic visit cancellations and no-shows. Conclusions: At a major tertiary-care hospital in Canada, key social determinant data such as ethnicity are not consistently available in the electronic health record of pediatric ophthalmology patients. Female sex, younger age, and living in a rural area or neighborhood with low-medium income quintile may be predictors of missed visits and require further study. Translational Relevance: This study highlights a need for improved documentation of social determinants of health variables in electronic health records.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it