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Record W2772200885

The EpiPen Problem: Analyzing Unethical Drug Price Increases and the Need for Greater Government Regulation

2017· article· en· W2772200885 on OpenAlexaboutno aff
Talal Rashid

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicPharmaceutical Economics and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessGovernment (linguistics)Public economicsDrugEconomicsPharmacologyMedicine
DOInot available

Abstract

fetched live from OpenAlex

In recent years, some pharmaceutical companies have started increasing the price of their existing drugs to exorbitant levels. Often, these drugs are medically necessary for patients, who are left to take on the high costs of the medicine. One recent example is Mylan, who raised the price of the EpiPen by four hundred percent, solely for the profit of its own company and to the detriment of consumers who rely on the EpiPen. Similar patterns of drug price increases have occurred in the past and will likely happen again in the future. This Comment will seek to identify the common elements of this pattern of increasing drug prices by looking at the behavior of corporations like Mylan and the way they operate, and it will assess current approaches to resolving this issue by looking at the roles of Congress, the Food and Drug Administration (FDA), and the Federal Trade Commission (FTC). The area of concern—apart from the way patients suffer from drug price increases—is that even after these companies are subjected to Congressional hearings to address their increasing drug prices, receive hefty fines from the FTC, experience bad press, and draw criticism about the issue of increasing drug prices, little change is made to resolve this problem. At the same time, industrialized nations around the world do not face the issue of increasing drug prices to the extent seen in the United States. Three countries—Canada, Switzerland, and France—have protected their citizens by structuring their healthcare system in a way that gives pharmaceutical companies little room to raise drug prices to high levels. These countries utilize approaches such as implementing a price ceiling, negotiating with pharmaceutical companies by looking at a drug’s therapeutic value, and setting a reassessment standard to periodically check on pharmaceutical companies. To this end, this Comment will look at these approaches in more detail and will analyze how they can be applied to the United States’ own healthcare system in a way that would prevent pharmaceutical companies from raising the prices of their drugs to unethical levels and, ultimately, lower the cost of prescription drugs

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 imitation

Not 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.

metaresearch head score (Codex)0.028
metaresearch head score (Gemma)0.130
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.033
Threshold uncertainty score0.147

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.130
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0060.011
Science and technology studies0.0060.021
Scholarly communication0.0110.018
Open science0.0030.005
Research integrity0.0100.014
Insufficient payload (model declined to judge)0.0070.000

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.

Opus teacher head0.047
GPT teacher head0.280
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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