Can you hear us now? The impact of health-care utilization by rare disease patients in the United States
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: The vast majority of rare diseases (RDs) are complex, disabling, and life-threatening conditions with a genetic origin. RD patients face significant health challenges and limited treatments, yet the extent of their impact within health care is not well known. One direct method to gauge the disease burden of RDs is their overall cost and utilization within health-care systems. METHODS: The 2016 Healthcare Cost and Utilization Project (HCUP) databases were used to extract health-care utilization data using International Classification of Diseases, Tenth Revision (ICD-10) codes. RESULTS: Of 35.6 million national hospital weighted discharges in the HCUP Nationwide Inpatient Sample, 32% corresponded to RD-associated ICD-10 codes. Total charges were nearly equal between RDs ($768 billion) compared to common conditions (CCs) ($880 billion) (p < 0.0001). These charges were a result of higher charges per discharge and longer length of stay (LOS) for RD patients compared to those with CCs (p < 0.0001). Health-care cost and utilization was similarly higher for RDs with pediatric inpatient stays, readmissions, and emergency visits. CONCLUSION: Pediatric and adult discharges with RDs show substantially higher health-care utilization compared to discharges with CCs diagnoses, accounting for nearly half of the US national bill.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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