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Record W2163907135 · doi:10.1093/jnci/dji243

Neuroblastoma, Well-Designed Evaluations, and the Optimality of Research Funding: Ask Not What Your Country Can Do for You …

2005· letter· en· W2163907135 on OpenAlexaff
Isra Levy

Bibliographic record

VenueJNCI Journal of the National Cancer Institute · 2005
Typeletter
Languageen
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsCanadian Medical AssociationUniversity of Ottawa
Fundersnot available
KeywordsAsk priceNeuroblastomaBusinessActuarial scienceBiologyFinanceGenetics

Abstract

fetched live from OpenAlex

Health care technology assessment (HTA) has garnered the attention of policy makers over the past 20 years because the introduction of new technologies is widely believed to have been a major driver of increased health care expenditures in developed countries ( 1 ) . Careful evaluation of health care technology prior to widespread introduction and diffusion has become a priority for policy makers who are committed to evidence-based decision making, and those involved with HTA have argued the merits of including economic evaluations alongside clinical effectiveness evaluations. It has been recognized that rigorous economic evaluations of new technologies may not always be feasible, and even if feasible the evaluation itself may not be cost-effective ( 2 ) . In this issue of the Journal, Soderstrom et al. ( 3 ) show that HTA-type research itself can be, as in their assessment of the economics of neuroblastoma screening evaluation, a remarkably cost-effective health care strategy from a societal perspective. Although extremely limited, the literature appears to support this conclusion, and Soderstrom leaves us with the tantalizing question of whether the current funding for well-designed HTA evaluations is sufficient. If not, he contends, the adverse health effects and wasteful health spending caused by the introduction of new but ineffective health services could be substantial. But how much assessment is enough? What should society spend on medical research as a whole? On health services research? On HTA? In the United States, the medical community and others have advocated strongly for increased federal funding of medical research. The American Medical Association, for example, has called for this funding to be “ample,” and to address the spectrum of basic biomedical research, translational research, clinical research and clinical trials, health services research, outcomes research, and prevention research ( 4 ) . These types of efforts have been successful for at least some categories in the research spectrum. Although the U.S. federal government expenditure in the National Institutes of Health is just 5% of its expenditures in the Centers for Medicare and Medicaid Services ( 5 ) , the NIH budget request for 2006 approaches $28.6 billion—an increase of more than 60% in just 5 years ( 6 ) . And private industry funds perhaps twice as much basic and preclinical biomedical research as the NIH ( 7 ) . Formal, publicly funded HTA funding has not fared as well. The Agency for Healthcare Research and Quality (AHRQ) aims to improve the effectiveness of health care for Americans through health services research and HTA. Its budget request for 2006 is $319 million ( 5 ) , about 1% of the NIH level of funding. And while some have suggested that federal support of health economics research has paralleled the growth in medical research in general, concerns have been expressed about gaps or duplications in some disease and intervention areas and lack of coordination of funding ( 8 ) . The state of HTA in the United States has been severely criticized, as “decentralized, fragmented, and duplicative,” with an “erratic commitment” by government to its funding ( 9 ) . Does the federal government spend the appropriate amount on the spectrum of health research? One view is that because of the escalating costs of scientific investigation the recent growth in funding, where it has occurred, has barely kept up with inflation ( 10 ) . Yet, in a global context the priorities and funding levels for research in the United States are arguably extravagant. Less than 10% of the world's medical research funds are used to address problems that are responsible for 90% of the world's burden of disease ( 11 , 12 ) . Furthermore, it has been argued that medical research has become increasingly inefficient and ineffective and that the current level of funding in developed countries is inflated and will “correct” downwards in the future, following a trajectory of a funding contraction in the wake of overexpansion ( 13 ) . More difficult than seeking to address Soderstrom's question about the appropriateness of research funding levels is grappling with the observation that it sometimes doesn't seem to matter anyway. Though the Quebec Neuroblastoma Screening Project (QNSP) was never funded as an HTA study (even if that is a context in which it can justifiably be acclaimed), it was an extraordinarily good societal investment for the U.S. taxpayer. The project prevented widespread implementation of an ineffective, harmful, and costly intervention in North America, and it contributed substantially to the discontinuation of a long-established neuroblastoma screening program in Japan. But HTA studies are seldom this effective in discouraging access to undesirable technologies. In the real world, policy-making decisions on the allocation of health care resources rest on social, political, and ethical dimensions that often cannot wait for well-designed scientific evaluations to deliver their results ( 2 , 14 , 15 ) . As for changing clinical attitudes, the congruent results of the QNSP and a similar well-designed and -conducted German study ( 16 ) have still been rejected by some physicians ( 17 ) . This phenomenon has been seen elsewhere, notably in the ongoing debates and controversies around the interpretation of breast cancer screening studies. But the outcome of the QNSP did have the desired result. Soderstrom demonstrates that the study averted unnecessary morbidity for literally thousands of children—and did so while returning a commendable yield on financial investment, even at seemingly very conservative sensitivity analyses. A disturbing question flows from this case study, though: What if screening had been an effective technology? If so, this study could, in retrospect, have been deemed an economic disaster. Conceivably, it might then have cost society many millions of dollars by delaying the prevention of morbidity and premature mortality through a useful and beneficial intervention. In that situation the justification for having done the study would have been purely philosophical. Neither lives nor dollars would have been saved by the research. But future policy, for both health systems and clinical interventions, would have been based on rigorous, unbiased information rather than speculation and anecdote. This is what makes medicine different from quackery. What then can we do in our community or academic practices? Advocate for more research funding—yes. But we have other obligations, too. The World Medical Association's Declaration of Helsinki states that “medical progress is based on research … the primary purpose [of which] is to improve prophylactic, diagnostic, and therapeutic procedures and the understanding of the etiology and pathogenesis of disease. Even the best proven prophylactic, diagnostic, and therapeutic methods must continuously be challenged through research for their effectiveness, efficiency, accessibility and quality” ( 18 ) . The medical community must embrace research. In the last 5 years the National Cancer Institute's spending has increased by 45%. During that same period the proportion of eligible adult cancer patients entering clinical research studies has remained static at less than 3%! In June 2005 the NCI announced plans to revamp its clinical trial system ( 19 ) , continuing to aid all physicians in encouraging participation in clinical trials. It behooves us to do our part—to actively participate in clinical and health services research and to take up results in our practice.

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)0.042
metaresearch head score (Gemma)0.170
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.958
Threshold uncertainty score0.223

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0420.170
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0060.010
Scholarly communication0.0080.010
Open science0.0030.004
Research integrity0.0900.069
Insufficient payload (model declined to judge)0.0060.002

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 head0.409
GPT teacher head0.543
Teacher spread0.135 · 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.

Study designNot applicable
DomainIncentives
GenreCommentary

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".

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Citations1
Published2005
Admission routes1
Has abstractno

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