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PANOVA-3: A phase 3 study of tumor-treating fields with gemcitabine and nab-paclitaxel for frontline treatment of locally advanced pancreatic adenocarcinoma.

2022· article· en· W4281682418 on OpenAlexaboutno aff
Vincent J. Picozzi, Teresa Macarulla, Philip Agop Philip, Carlos Becerra, Tomislav Dragovich

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGemcitabinePancreatic cancerOncologyInternal medicinePaclitaxelConcomitantChemotherapyCancer

Abstract

fetched live from OpenAlex

TPS4187 Background: Tumor Treating Fields (TTFields) are a novel, locoregional antimitotic treatment modality approved for glioblastoma and malignant pleural mesothelioma. Continuous, non-invasive low intensity, intermediate frequency (150–200 kHz) alternating electric fields are delivered to the tumor via skin-placed arrays. In vitro, TTFields (150 kHz), with or without chemotherapy, induced antiproliferative and anticlonogenic activity on pancreatic cancer cell lines (Giladi M, et al. Pancreatology 2014;14:54–63). The phase 2 PANOVA study (NCT01971281) demonstrated that the combination of TTFields with nab-paclitaxel and gemcitabine (GnP) is well-tolerated, with promising efficacy in metastatic and locally advanced pancreatic adenocarcinoma (LAPC) (Rivera F, et al. Pancreatology 2019;19:64–72). These data indicate that TTFields with GnP warrant phase 3 evaluation. Methods: PANOVA-3 (NCT03377491) is a prospective, randomized, phase 3 trial designed to investigate the efficacy and safety of TTFields concomitant with GnP in patients with LAPC. Planned enrollment is 556 patients. Eligibility criteria include unresectable LAPC (per National Comprehensive Cancer Network guidelines), Eastern Cooperative Oncology Group performance status of 0–2, and no prior treatment. Patients will be stratified by performance status and geographical region, and randomly assigned 1:1 to TTFields plus GnP or GnP alone. Based on a recent protocol amendment, a smaller and lighter-weight (reduced from 6 to 2.7 lbs) TTFields device will be used. Standard doses of nab-paclitaxel (125 mg/m2) and gemcitabine (1000 mg/m2) will be administered on days 1, 8, and 15 of a 28-day cycle. TTFields (150 kHz) will be delivered ≥ 18 h/day until local disease progression per Response Evaluation Criteria in Solid Tumors V1.1. Follow-up will be performed every 4 weeks and a computed tomography scan of the chest and abdomen every 8 weeks. After local disease progression, patients will be followed every month until death. The primary endpoint is overall survival (OS). Secondary endpoints include progression-free survival (PFS), local PFS, objective response rate, 1-year survival rate, pain- and puncture-free survival rate, rate of resectability, quality of life, and toxicity. The sample size was estimated per log-rank test comparing time to event in patients treated with TTFields plus GnP with published clinical trial data on patients treated with GnP alone. PANOVA-3 is designed to detect a hazard ratio of 0.75 in OS. Type I error is set to 0.05 (2-sided) and power to 80%. The trial is currently recruiting at 106 sites in Austria, Belgium, Canada, China, Croatia, Czech Republic, France, Germany, Hong Kong, Hungary, Israel, Italy, Poland, Spain, Switzerland, and USA. The DMC last reviewed the trial in August 2021, and suggested that the trial continue as planned. Clinical trial information: NCT03377491.

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)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0060.001

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 head0.083
GPT teacher head0.441
Teacher spread0.358 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
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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Citations1
Published2022
Admission routes1
Has abstractyes

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