Geospatial analysis of accessibility to oculofacial plastic surgery in the United States: Driving distance and sociodemographic disparities
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
To identify disparities in access to complex oculofacial plastic care by mapping American Society of Ophthalmic Plastic and Reconstructive Surgery (ASOPRS) members’ service coverage areas (SCAs) in the United States (US). Cross-sectional analysis We analyzed US-based ASOPRS members’ practice locations in ArcGIS Pro (Esri) to define SCAs as regions within a 60-minute drive. With American Community Survey data and chi-square tests, we compared social determinants of health within and outside SCAs. Of the 322,561,852 Americans, 260,154,031 (80.7%) lived within a 60-minute driving time from one of the 635 ASOPRS members. The population outside 60-minute SCAs was significantly more likely to be White, Non-Hispanic, without university education, receiving social security income, residing in a household below federal poverty level, and lacking health insurance, compared to the population inside SCAs (each P<0.001). States with the most ASOPRS members were California (n=95, 2.4 per million residents), Texas (n=47, 1.5/million) and Florida (n=45, 2.0/million), while none practiced in Montana, North Dakota, South Dakota, New Mexico and Wyoming. Inequitable geographic distribution of ASOPRS members disproportionately affects patients in rural areas and those with lower socio-economic status. Recognizing these geographic-social obstacles can inform policies to reduce barriers to complex oculofacial plastic care access.
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 | 0.000 | 0.001 |
| 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 | 0.000 | 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