<p>Medication Utilization Patterns 90 Days Before Initiation of Treatment with Repository Corticotropin Injection in Patients with Infantile Spasms</p>
Bibliographic record
Abstract
INTRODUCTION: (vigabatrin). Given real-world variation in treatment of patients with IS, this study characterized treatment patterns with IS medications and determined all-cause health care resource utilization (HCRU) during the 90 days before initiating therapy with RCI in patients with IS. MATERIALS AND METHODS: Research Databases were used to identify commercially insured US patients <2 years of age at RCI initiation with an IS diagnosis, per label use, from 1/1/07 to 12/31/15; presence of an electroencephalogram following diagnosis was required to assure diagnosis. Diagnosis codes and dispensed IS treatments of interest (drug classes including corticosteroids, vigabatrin, and other antiepileptic drugs [AEDs] excluding vigabatrin) before RCI initiation were evaluated. RESULTS: The 5 most common diagnoses other than IS observed in the study cohort (n=422) were "other convulsions," "acute upper respiratory infection," "esophageal reflux," "epilepsy, unspecified," and "abnormal involuntary muscle movements." Among the study cohort, 51.7% received RCI first; 38.9% received 1 drug class and 9.5% received >1 drug class before RCI initiation. Other AEDs were dispensed most often, either alone (31.3%) or with other drug classes (9.3%). Mean HCRU included 11.8 all-cause outpatient visits and 4.5 medications dispensed. Patients who received RCI or corticosteroids as their initial IS treatment had the lowest and second-lowest HCRU. CONCLUSION: In the 90 days before initiating RCI, patients with IS received multiple diagnoses and treatments, characterized by frequent HCRU. Use of RCI first (no prior IS medications) and AEDs first were associated with the lowest and highest HCRU, respectively, across all categories (all-cause outpatient visits, emergency department visits, hospital admissions, prescription medications).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".