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Record W4383872841 · doi:10.2217/imt-2022-0257

An Assessment of Extended Pembrolizumab Dosing in Advanced Non-Small-Cell Lung Cancer in the Covid-19 Pandemic

2023· article· en· W4383872841 on OpenAlexaff
Gordon Taylor Moffat, Lilian Hanna, Wilma M. Hopman, Andrea S. Fung, Pierre-Olivier Gaudreau

Bibliographic record

VenueImmunotherapy · 2023
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsQueen's UniversityHealth Sciences CentreSunnybrook Health Science CentrePrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsPembrolizumabMedicineDosingLung cancerAdverse effectInternal medicineCancerRetrospective cohort studyToxicityGastroenterologyImmunotherapy

Abstract

fetched live from OpenAlex

Background: There are limited clinical data comparing extended dosing (ED) versus standard dosing (SD) of pembrolizumab for metastatic non-small-cell lung cancer. Methods: This retrospective study included patients with metastatic non-small-cell lung cancer and PD-L1 tumor proportion score ≥50% treated with one or more cycles of single-agent pembrolizumab with SD or ED from January 2018 to December 2020. Results: A higher proportion of patients were alive in the ED group (vs SD) at 6 months (94 vs 51%), 12 months (94 vs 33%) and data cutoff (94 vs 26%) (p < 0.001 for all). The rate (44 vs 32%; p = 0.407) and severity of grade ≥3 immune-related adverse events were similar (50 vs 52%); however, ED patients more frequently discontinued treatment due to toxicity (45 vs 15%; p < 0.001). Conclusion: A greater proportion of ED patients were alive at data cutoff, and the rate and severity of immune-related adverse events were similar between groups.

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)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.032
GPT teacher head0.402
Teacher spread0.371 · 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 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

Citations3
Published2023
Admission routes1
Has abstractyes

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