Changes in Medical Services and Drug Utilization and Associated Costs After Narcolepsy Diagnosis in the United States.
Bibliographic record
Abstract
BACKGROUND: Healthcare utilization and the cost implications associated with undiagnosed and/or misdiagnosed narcolepsy have not been evaluated, and there is scant literature characterizing the newly diagnosed population with narcolepsy with respect to treatment patterns and resource utilization. OBJECTIVE: To analyze the changes in medication use, healthcare utilization, and the associated costs after a new diagnosis of narcolepsy. METHODS: In this retrospective cohort study, we used data from the Truven Health Analytics MarketScan Research Databases, between January 2006 and March 2013, to identify patients who had a probable new diagnosis of narcolepsy-defined as a de novo medical claim for a multiple sleep latency test-which was preceded by ≥6 months of continuous insurance and was followed by a de novo diagnosis of narcolepsy. The utilization and cost of medical services and the percentage of patients filling prescriptions for narcolepsy-related medications were evaluated in 3 consecutive 1-year periods from the date of a positive multiple sleep latency test result (ie, index date), and each year's findings were compared with the annualized results from the 6-month preindex period. RESULTS: <.0001 vs preindex). CONCLUSIONS: In this study, the confirmation of a diagnosis of narcolepsy was associated with decreasing utilization and associated costs of medical services in the first 3 years after diagnosis. The total costs encompassing medical services and pharmacy costs were relatively stable during this period.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".