DRUG DISINVESTMENT FRAMEWORKS: COMPONENTS, CHALLENGES, AND SOLUTIONS
Bibliographic record
Abstract
OBJECTIVES: Value assessments of marketed drug technologies have been developed through disinvestment frameworks. Components of these frameworks are varied and implementation challenges are prevalent. The objective of this systematic literature review was to describe disinvestment framework process components for drugs and to report on framework components, challenges, and solutions. METHODS: A systematic literature search was conducted using the terms: reassessment, reallocation, reinvestment, disinvestment, delist, decommission or obsolescence in MEDLINE, EMBASE, NLM PubMed, the Cochrane Library, and CINAHL from January 1, 2000, until November 14, 2015. Additional citations were identified through a gray literature search of Health Technology Assessment international (HTAi) and the International Network of Agencies for Health Technology Assessment (INAHTA) member Web sites and from bibliographies of full-text reviewed manuscripts. RESULTS: Sixty-three articles underwent full text review and forty were included in the qualitative analysis. Framework components including disinvestment terms and definitions, identification and prioritization criteria and methods, assessment processes, stakeholders and dissemination strategies, challenges, and solutions were compiled. This review finds that stakeholders lack the political, administrative, and clinical will to support disinvestment and that there is not one disinvestment framework that is considered best practice. CONCLUSIONS: Drug technology disinvestment components and processes vary and challenges are numerous. Future research should focus on lessening value assessment challenges. This could include adopting more neutral framework terminology, setting fixed reassessment timelines, conducting therapeutic reviews, and modifying current qualitative decision-making assessment frameworks.
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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 | 0.204 | 0.263 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.015 | 0.019 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.019 | 0.026 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".