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Record W4310117556 · doi:10.1182/blood-2022-158327

Ethical Prioritization of Patients to CAR T-Cell Therapies: Tool Development and Process

2022· article· en· W4310117556 on OpenAlexaff
Christine I. Chen, Jennifer Bell

Bibliographic record

VenueBlood · 2022
Typearticle
Languageen
FieldMedicine
TopicBiomedical Ethics and Regulation
Canadian institutionsUniversity of TorontoUniversity Health NetworkPrincess Margaret Cancer Centre
Fundersnot available
KeywordsAccountabilityTransparency (behavior)Health careMedicineProcess managementBusinessPolitical science

Abstract

fetched live from OpenAlex

Introduction: CAR T-cell therapy is a novel and expensive anti-cancer treatment marked by limited availability and an expanding eligible patient population. This situation requires an ethical framework and priority-setting process to guide fair allocation of limited healthcare resources. The purpose of this health services and quality initiative was to develop an ethical prioritization tool to support oncology clinicians in making fair and equitable decisions about patient prioritization to CAR T-cell therapies. Methods: Accountability for reasonableness (A4R) is a well-developed ethical approach to resource allocation when there are values-disagreements between stakeholders about the principles that should govern priority setting. A4R supports a democratic decision-making process, enabling stakeholders to agree on what is legitimate and fair, rather than articulating principles that should govern decision-making a priori. Key elements of the process include: transparency about the rationale for decisions, rationales and evidence that all can accept as relevant to meeting health needs fairly, and procedures for revisiting decisions. Following the A4R approach, we engaged a strong multi-disciplinary team to form a CAR T-cell therapy Ethics Working Group that included physicians, nurses, patients, coordinators, social work, managers, operations, flow, quality, bioethics, and fellows/trainees. Stakeholders from the myeloma, lymphoma and leukemia programs were represented. Through two deliberative events and one patient focus group we established an ethical framework, prioritization tools based on the ethical framework, and an assessment process to support fair and equitable decision making. Results: Stakeholders agreed the following ethical principles are relevant to the decision-making process: beneficence, non-maleficence, equity/consistency, stewardship, autonomy, evidence-informed, and utility. Beneficence and non-maleficence were identified as inherent to the process and equity/consistency and stewardship were also identified as extremely important, especially as more patients become eligible for CAR-T cell therapy. Stakeholders then identified prioritization criteria and categorized them into four major themes: medical benefit, safety/risk of complications, medical urgency, and psychosocial factors. Although the relative weights were not determined, the sequence of application of these criteria were deemed important. Determination of medical benefit was primary, as no patient should proceed further if they did not have any potential for medical benefit. Safety/Risk and psychosocial evaluation would follow using existing tools (e.g., ECOG) and allowing for summative objectives, when possible. Lastly, medical urgency as defined by rapid pace of disease progression, refractoriness to prior therapy, and development of serious disease-related complications, was considered critical in prioritizing patients for therapy. However, stakeholders agreed and cautioned that medical urgency does not equate medical benefit, as patients with rapidly expanding disease and acute deterioration frequently have dismal outcomes, despite all attempts to expedite their treatment course. Recognizing the importance of sequencing in the assessment of criteria, stakeholders developed a 3-Step assessment process: (1) Medical Benefit Assessment, (2) Safety/Risk and Psychosocial Assessment and (3) referral to the Cell Therapy Review Committee for review and prioritization based on medical urgency. Patients who may gain medical benefit, have reasonable risk of toxicity, and have adequate psychosocial capacity and support from Steps 1 and 2, proceed to 3 where consensus decisions are documented on an existing intake form. Conclusions: An ethical framework to support CAR T-cell priority-setting is necessary to ensure transparent and equitable decision making, thus allowing fair patient access to these important therapies. It also enhances the cancer care experience for all by promoting public trust and confidence in the healthcare system and therapeutic relationships by ensuring that decisions are based on sound rationales and that individual clinician biases are minimized.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.466
Threshold uncertainty score0.139

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.261
Teacher spread0.251 · 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".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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