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Record W2998229904 · doi:10.1017/s0266462319001636

OP135 CAR T-cell Therapy HTA Informs Australian Policy

2019· article· en· W2998229904 on OpenAlexaboutno aff
Paul Fennessy, Vanessa Clements

Bibliographic record

VenueInternational Journal of Technology Assessment in Health Care · 2019
Typearticle
Languageen
FieldMedicine
TopicBiomedical Ethics and Regulation
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineWorkforceAdverse effectEuropean unionRevenueClinical trialBusinessHealth technologyFinanceHealth careEconomic growthInternal medicine

Abstract

fetched live from OpenAlex

Introduction Chimeric antigen receptor (CAR) T-cell therapy is offered as a once-only treatment for patients with certain cancers that are not responsive to standard treatment. While clinicians, patients and their families increasingly seek access to CAR T-cell therapy, there is no revenue stream to support access through public or private health systems. Methods The New South Wales (NSW) Ministry of Health and Victorian Department of Health and Human Services oversighted a health technology assessment (HTA) to explore the status and geography of regulatory frameworks supporting delivery of CAR T-cell therapy, evidence for the safety, efficacy and cost, clinical trials conducted or underway and manufacturing aspects. Results CAR T-cell therapies are approved in the European Union and United States of America, and being considered in Australia, Canada, China and Japan. Efficacy, safety and cost-effectiveness is limited by the size and single-arm design of early stage trials and variation between them. While overall response ranges from 36–93 percent, early results for some cancers are less favorable. Durability of treatment effect is unknown, adverse events are common and can be life-threatening and risk of delayed onset toxicity remains unknown. Treatment requires access to approved manufacturing facilities (none in Australia) and specialist clinical staff. Conclusions CAR T-cell therapy is promising and demand is increasing, but the limited safety profile and evidence base should mitigate policy and investment decisions. Broader consideration should be given to developing, or identifying access to, manufacturing and clinical workforce capability and capacity to meet national demand. Australia is likely to encounter similar issues in other jurisdictions, such as limited evidence base and complex safety issues. Factors to be considered on a local and national basis for assessment and implementation include: (i) Regulatory support for industry; (ii) Strategies to manage uncertainties in long-term risks, benefits and costs; (iii) Access to accredited manufacturing facilities; (iv) Developing clinical and manufacturing workforce capability and capacity.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.533
Threshold uncertainty score0.433

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.414
Teacher spread0.398 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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