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Record W283351920

Decision-making Processes Regarding Cancer Technologies: A Review

2008· review· en· W283351920 on OpenAlexaboutno aff
Tania Stafinski, George P. Browman, Devidas Menon

Bibliographic record

VenueDigitalGeorgetown (Georgetown University Library) · 2008
Typereview
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsnot available
Fundersnot available
KeywordsScrutinyEquity (law)Public relationsHealth carePsychological interventionPoliticsQuality (philosophy)BusinessMedicinePolitical scienceLawNursing
DOInot available

Abstract

fetched live from OpenAlex

The issue of timely access to high quality cancer care has heightened in Canada, fueled in part by incidence rates reaching levels where cancer now touches, in some way, the lives of most Canadians. (1) Thus, deliberations over how best to maximize the health of cancer patients are often emotionally and politically charged. Two major issues have been highlighted through the media and the political process: (1) timely access to required services (i.e., waiting times for radiation, surgery and specialist consultation), (2) limits placed on equitable access to services such as diagnostic interventions (e.g., MRI and PET) and (3) cancer drug treatments. As cancer agencies across the country look for ways to provide timely access to technologies that deliver high quality care, they do so facing unlimited demands, limited budgets, and intense scrutiny by both those affected and the media. There seems to be a growing lack of public confidence in decisions about which technologies to publicly fund, particularly in the case of promising, often high cost innovations championed by physicians, patients and manufacturers. As a result, policy-makers charged with the task of ensuring prudent and principled use of scarce resources have become demonized, since the essence of priority-setting means that access to some will be denied. It has become increasingly clear that approaches to priority-setting require a blending of two decisional domains: effectiveness and efficiency (the evidence-based paradigm) and equity/fairness (the values/ethics based paradigm). What remains unclear are ways of marrying the two so that coverage decisions may be deemed legitimate and fair by all stakeholders. Over the past three decades, an international body of work focusing on resource allocation and technology funding decision-making has emerged in response to the increasing pressures faced by governments to adopt new and innovative technologies into their health care systems. (2) It contains research which draws from many disciplines including: clinical medicine; epidemiology; political, social, behavioral and management sciences; economics; and ethics. This work examines how decisions are actually made (i.e., descriptive processes), how they should be made (i.e., normative processes), and tools for aiding or informing these processes. (3) While the majority of this work has taken place outside of the cancer context, it addresses issues of a similar nature and complexity. This paper provides an overview of this work to inform both scholars (in ethics, law and health services research) and decision-makers in Canadian health care systems. Descriptive Processes Canadian research describing how funding recommendations or decisions for new cancer technologies are actually made is limited to a few case studies in Ontario. (4) Factors shaping decisions were identified through a combination of document analysis, interviews of Cancer Care Ontario's Policy Advisory Committee, and observations of their meetings. These factors included: benefit to patients and its magnitude, quality of evidence (i.e., the degree of certainty of the benefit), existence of alternatives, treatment duration, total population of patients affected, total cost to the system, pressure from physician and patient groups, and historical precedent. Cost-effectiveness was not used, but the committee discussed the concept of value-for-money. Although the committee had initially considered developing a list of funding recommendations, ranking them, comparing the costs against a known fixed budget, and then drawing a line at the end of the resources, it abandoned the idea when members failed to agree upon potential priority measures. (5) A similar study was conducted in the United Kingdom, where a specialist cancer hospital and a consortium of six regional health authorities set priorities for funding new 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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.212
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0070.001
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0000.005
Open science0.0030.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.003

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 head0.201
GPT teacher head0.387
Teacher spread0.186 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreReview

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
Published2008
Admission routes1
Has abstractyes

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