A spatial analysis of amyotrophic lateral sclerosis (ALS) cases in the United States and their proximity to multidisciplinary ALS clinics, 2013
Bibliographic record
Abstract
Background: Amyotrophic lateral sclerosis (ALS) is a fatal motor neuron disease that typically results in death within 2–5 years of initial symptom onset. Multidisciplinary ALS clinics (MDCs) have been established to provide specialty care to people living with the disease. Objective: To estimate the proximity of ALS prevalence cases to the nearest MDC in the US to help evaluate one aspect of access to care. Methods: Using 2013 prevalence data from the National ALS Registry, cases were geocoded by city using geographic information system (GIS) software, along with the locations of all MDCs in operation during 2013. Case-to-MDC proximity was calculated and analyzed by sex, race, and age group. Results: During 2013, there were 72 MDCs in operation in 30 different states. A total of 15,633 ALS cases were geocoded and were distributed throughout all 50 states. Of these, 62.6% were male, 77.9% were white, and 76.2% were 50–79 years old. For overall case-to-MDC proximity, nearly half (44.9%) of all geocoded cases in the US lived >50 miles from an MDC, including approximately a quarter who lived >100 miles from an MDC. There was a statistically significant difference between distance to MDC by race and age group. Conclusions: The high percentage of those living more than 50 miles from the nearest specialized clinic underscores one of the many challenges of ALS. Having better access to care, whether at MDCs or through other modalities, is likely key to increasing survivability and obtaining appropriate end-of-life treatment and support for people with ALS.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".