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Record W4415459514 · doi:10.1002/hon.70144

The French Experience of Pharmacists and CAR T‐Cells: A Study of the French Society of Oncology Pharmacy (SFPO)

2025· article· en· W4415459514 on OpenAlexaboutno aff
Vérane Schwiertz, Romain de Jorna, Adeline Quintard, Marie‐Antoinette Lester, Marine Pinturaud, Nicolas Cormier, Élise D’Huart, Emmanuelle Fougereau, Muriel Carvalho, Benjamin Sourisseau, Pauline Gueneau, Mathieu Wasiak, Alexia Jouvance, Muriel Paul, R. Chevrier, Bertrand Pourroy, Jean‐Louis Cazin, Florence Ranchon, Isabelle Madelaine‐Chambrin, Catherine Rioufol

Bibliographic record

VenueHematological Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsnot available
Fundersnot available
KeywordsPharmacyObservational studySoftware deploymentRetrospective cohort studyPharmacy practicePharmacistMEDLINE

Abstract

fetched live from OpenAlex

The aim of this study was to describe the initial 3-year experience in vein-to-vein time for axi-cel therapy and the role of pharmacists in the first recruiting French centers. Retrospective observational data were collected for vein-to-vein time for commercial axi-cel after ≥ 2 lines of systemic therapy between January 2019 and December 2021 in the first 12 authorized French centers. Hospital pharmacists used a circuit database to ensure the prospective traceability at all steps. Totally 501 of the 562 intention-to-treat registrations on the database for cytapheresis (89,1%) led to the infusion of axi-cel. Median vein-to-vein time was shortened by 4 days. This was mainly due to tightening the interval from apheresis to release. The 36-day median vein-to-vein time achieved after 3 years' experience should be compared to the 29-34 days reported in Canada, the USA and Israel, where manufacturing sites are geographically closer to hospital centers than they are in France. The top 5 recruiting centers had the shortest vein-to-vein times. This French experience may serve as a model for other European centers, notably as regards deployment of pharmacists to improve the patient pathway with CAR T-cells and other gene and cellular therapies.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.181
Threshold uncertainty score0.682

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.077
GPT teacher head0.441
Teacher spread0.364 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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