High rates of physician services utilization at least five years before multiple sclerosis diagnosis
Bibliographic record
Abstract
BACKGROUND: Multiple sclerosis (MS) management has changed over time, but changes in health care utilization by MS patients remain understudied. We estimated physician services utilization in the five-year periods before and after MS diagnosis, and over the period 1984-2008. METHODS: Using administrative data we identified 4092 persons with MS and a matched general population (GPOP) cohort of 21,446 persons. Using general linear models we compared physician visits between the MS and GPOPs for the period 1984-2008, the year of MS diagnosis, and for the five-year periods pre- and post-diagnosis. RESULTS: From 1984 to 2008, 98% of the MS population averaged ≥1 physician visits/year versus 87% of the GPOP. In 2008, the MS population had 12.9 physician visits/person-year while the GPOP had 8.4 (rate ratio (RR) 1.53; 95% confidence interval (CI): 1.52-1.55). Five years pre-MS diagnosis, the MS population had more physician visits than the GPOP (RR 1.15; 95% CI; 1.10-1.21). The number of visits peaked the year of MS diagnosis (19.0), decreasing thereafter, but remaining elevated versus the pre-diagnosis period. CONCLUSION: The MS population uses more physician services than the GPOP, starting at least five years pre-MS diagnosis. A better understanding of the reasons for these higher utilization rates may ultimately improve outcomes in MS.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".