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Record W4391982704 · doi:10.7150/jca.92624

Efficacy and Safety Assessment of Intrathoracic Perfusion Chemotherapy Combined with immunological factor Interleukin-2 in the Treatment of Advanced Non-Small Cell Lung Cancer: A Retrospective Cohort Study

2024· article· en· W4391982704 on OpenAlexaff
Qiang Cao, Jinyi Zhu, Xinyan Wu, Jiapeng Li, Yuquan Chen, Yanwei You, Xiaochen Li, Xufeng Huang, Yujie Zhang, Rizhu Li, Dan Han

Bibliographic record

VenueJournal of Cancer · 2024
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Diagnosis and Treatment
Canadian institutionsUniversity of Toronto
FundersNanjing Drum Tower HospitalNanjing UniversityGovernment of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsMedicineRegimenLung cancerInternal medicineOncologyRetrospective cohort studyGemcitabineOxaliplatinResponse Evaluation Criteria in Solid TumorsAdverse effectChemotherapyChemotherapy regimenSurgeryCancerProgressive diseaseColorectal cancer

Abstract

fetched live from OpenAlex

Objective: This study evaluated the efficacy and safety of the gemcitabine and oxaliplatin intrathoracic perfusion chemotherapy (IPCGOR) regimen combined with interleukin-2 (IL-2) for advanced non-small cell lung cancer (NSCLC). Methods: We conducted a retrospective analysis of 460 advanced NSCLC patients from the Yunnan Province Early Cancer Diagnosis and Treatment Project (June 2020-October 2022), assessing the IPCGOR and IL-2 combination. Outcomes were measured based on RECIST 1.1 criteria, focusing on objective response rate (ORR), disease control rate (DCR), median progression-free survival (mPFS), median overall survival (MOS), and treatment safety. Results: The treatment demonstrated an ORR of 67.4%, a DCR of 97.4%, an mPFS of 8.5 months, and an MOS of 12.5 months. 14 patients underwent successful surgery post-treatment. Common adverse reactions were manageable, with no treatment-related deaths reported.

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)0.003
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 score0.002
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.009
GPT teacher head0.340
Teacher spread0.331 · 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

Citations18
Published2024
Admission routes1
Has abstractyes

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