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Point-of-care CAR T manufacturing solutions: can 1 model fit all?

2024· article· en· W4402471159 on OpenAlexaff
Álvaro Urbano-Ispizúa, Nirav N. Shah, Natasha Kekre

Bibliographic record

VenueBlood Advances · 2024
Typearticle
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsOttawa Hospital
Fundersnot available
KeywordsPoint (geometry)Computer scienceMathematics

Abstract

fetched live from OpenAlex

Point: academic point of care alone can bring equitable and affordable access to CAR T-cell therapeutics One of the most important advances in the 21st century in the fight against cancer has been the development of chimeric antigen receptor (CAR) T cells in the treatment of acute lymphoblastic leukemia (ALL), non-Hodgkin lymphoma (NHL), and multiple myeloma (MM).Unfortunately, to date, access to this lifesaving therapy has been limited to very few patients worldwide.The reasons for this are, among others, their very high cost, the lack of interest of pharmaceutical companies in small or lowgross domestic product countries, the limited number of CAR T cells that can be prepared in a centralized manufacturing model, and the endemic slow incorporation of innovative, complex, and expensive treatments in many health systems.The pharmaceutical model of CAR T cells is focused on meeting the values of company shareholders, not filling the needs of patients who have no treatment options left.Here, we will detail the academic models looking to solve this dilemma.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.501
Threshold uncertainty score0.486

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 head0.045
GPT teacher head0.339
Teacher spread0.294 · 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 designBench or experimental
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

Citations16
Published2024
Admission routes1
Has abstractyes

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