Geospatial analysis of accessibility to oculofacial plastic surgery in the United States: Driving distance and sociodemographic disparities
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.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".