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Record W3136918147 · doi:10.7150/jca.55322

Intratumoral Treatment with Chemotherapy and Immunotherapy for NSCLC with EBUS-TBNA 19G

2021· article· en· W3136918147 on OpenAlexaff
Paul Zarogoulidis, Wolfgang Hohenforst‐Schmidt, Haidong Huang, Jun Zhou, Qin Wang, Xiangqi Wang, Ying Xia, Yin-feng DING, Chong Bai, Christoforos Kosmıdis, Konstantinos Sapalidis, Chrysanthi Sardeli, Kosmas Tsakiridis, Bojan Zarić, Tomi Kovačević, Vladimir Stojšić, Tatjana Šarčev, Daliborka Bursać, Biljana Kukić, Sofia Baka, Evagelia Athanasiou, Dimitris Hatzibougias, Electra Michalopoulou-Manoloutsiou, Savvas Petanidis, Dimitris Drougas, Konstantinos Drevelegas, Dimitrios Paliouras, Nikolaos Barbetakis, Anastasios Vagionas, Lutz Freitag, Aimilios Lallas, Ioannis Boukovinas, Dimitris Petridis, Aris Ioannidis, Dimitris Matthaios, Κonstantinos Romanidis, Chrisanthi Karapantzou

Bibliographic record

VenueJournal of Cancer · 2021
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsCytodiagnostics (Canada)
Fundersnot available
KeywordsMedicineImmunotherapyChemotherapyOncologyCancer researchInternal medicineCancer

Abstract

fetched live from OpenAlex

Immunotherapy is being used for the past five years either as first line or second line treatment with great results. Chemotherapy and radiotherapy have been also used as combination to immunotherapy to further enhance this type of treatment. Intratumoral treatment has been previously proposed as a treatment option for certain non-small cell lung cancer patients. Patients and Methods: We recruited in total seventy four patients with non-small cell lung cancer in their second line treatment who received only chemotherapy in their first line treatment with programmed death-ligand-1 50. Only adenocarcinoma or squamous cell carcinoma, and all negative for epidermal growth factor receptor, anaplastic lymphoma kinase, proto-oncogene tyrosine-protein kinase-1 and proto-oncogene B-Raf. Data were first examined with descriptive statistics choosing frequencies for categorical variables and histograms for the continuous ones. Twenty five received only intravenous immunotherapy and forty-nine intravenous cisplatin with immunotherapy. Data were first examined with descriptive statistics choosing frequencies for categorical variables and histograms for the continuous ones.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0040.001

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 head0.010
GPT teacher head0.334
Teacher spread0.324 · 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

Citations13
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueJournal of CancerSame topicLung Cancer Treatments and MutationsFrench-language works237,207