Trends in Teprotumumab Insurance Authorization and Socioeconomic Determinants of Teprotumumab Access
Bibliographic record
Abstract
Background: Teprotumumab is a novel biologic Food and Drug Administration (FDA)-approved medication for thyroid eye disease (TED). Cost remains a significant barrier to medication access. The present study analyzed the contribution of patient socioeconomic and clinical factors on access and eligibility for TED treatment with teprotumumab. Methods: This study is a retrospective chart review of 93 TED patients receiving care at a tertiary care academic hospital between December 2019 and December 2023, for whom a prior authorization (PA) for teprotumumab treatment was submitted. We collected sociodemographic data, smoking status, insurance type, clinical activity score (CAS), and prior attempted treatments, as well as PA approval status and reason for denial, if applicable. Data were compared between patients approved or denied PA at the first request using a t -test, Fisher's exact test, and descriptive analysis, as appropriate. Results: PA was denied for 13 patients (14%). PA was significantly more likely to be approved for patients with Medicare coverage and denied for those Medi-Cal coverage and geographically further away from the hospital. Five PAs were denied in 2020 (38%), five in 2021 (38%), two in 2022 (15%), and one in 2023 (8%). Common reasons for denial included collection of thyroid labs over 30 days prior to the request (n = 4, 31%), lack of prior oral corticosteroid trial (n = 3, 23%), and administrative error (n = 3, 23%). Ten (77%) patients received subsequent approval. Conclusions: Insurance type, geographic location, and socioeconomic status are important factors that may affect teprotumumab authorization. PA denials decreased after 2022, likely secondary to updated TED guidelines and drug availability. Compliance with insurance requirements may streamline the authorization process and increase PA approval rates. J Endocrinol Metab. 2024;14(4):159-165 doi: https://doi.org/10.14740/jem997
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.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".