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Record W3155753566 · doi:10.1016/s1470-2045(21)00076-0

Chemotherapy and radiotherapy in locally advanced head and neck cancer: an individual patient data network meta-analysis

2021· article· en· W3155753566 on OpenAlexaff
Claire Petit, Benjamin Lacas, Jean‐Pierre Pignon, Quynh‐Thu Le, Vincent Grégoire, Cai Grau, Allan Hackshaw, Björn Zackrisson, Mahesh Parmar, Ju-Whei Lee, Maria Grazia Ghi, Giuseppe Sanguineti, Stéphane Temam, M. Cheugoua-Zanetsie, Brian O’Sullivan, Marshall R. Posner, Everett E. Vokes, Juan Jesús Cruz, Z. Szutkowski, Éric Lartigau, Volker Budach, Rafał Suwiński, Michael Poulsen, Shaleen Kumar, Sarbani Ghosh Laskar, Jean‐Jacques Mazeron, Branislav Jeremić, John Simes, Lai‐ping Zhong, Jens Overgaard, Catherine Fortpied, Pedro A. Torres‐Saavedra, Jean Bourhis, Anne Aupérin, Pierre Blanchard, David J. Adelstein, J. Agarwal, M. Alfonsi, Athanassios Argiris, A. Aupérin, Andrea Bacigalupo, Voichita Bar‐Ad, H. Bartelink, Beth M. Beadle, Y. Belkacemi, René‐Jean Bensadoun, J. Bernier, Åse Bratland, David M. Brizel, W. Budach, Barbara Burtness, G. Calais, Brittany Campbell, Jimmy J. Caudell, Sylvie Chabaud, Emmanuel Chamorey, Devendra Chaukar, K.H. Cho, O. Choussy, James W. Denham, W. Dobrowsky, M.M. Dominello, C.M.L. Driessen, C. Fallai, Arlene A. Forastiere, George Fountzilas, P. Garaud, A.S. Garden, B. Géry, Pirus Ghadjar, Maria Ghi, P. Graff-Cailleaud, A. Hackshaw, E. Haddad, Bruce G. Haffty, Aaron R. Hansen, John Hay, Stefanie Hayoz, Jean-Claude Horiot, Ricardo Hitt, Jørgen Johansen, Christopher U. Jones, M. Juliéron, Claus Andrup Kristensen, Johannes A. Langendijk, M. Lapeyre, Lisa Licitra, J.W. Lee, Phillip Lee, Freddi Lewin, Y. Li, Alberto Lopes, Mohamed Lotayef, B. Maciejewski, Samir Mehta, Wojciech Michalski, James Moon, Sung Ho Moon, Elizabeth Moyal, Matthew Nankivell, Per Nilsson, P. Olmi, Roberto Orecchia, Max Parmar, Y. Pointreau, Harvey Quon, S. Racadot, David I. Rosenthal, P Rovea, Maria Grazia Ruo Redda, G. Shenouda, Atul Sharma, Christian Simon, C. Sire, Krzysztof Składowski, S. Spencer, S. Staar, P. Strojan, C. Stromberger, Zoltán Takácsi‐Nagy, Yungan Tao, David Thomson, Jeffrey Tobias, Valter Torri, Lee Tripcony, A. Trotti, Vassilikì Tseroni, C. van Herpen, Harm van Tinteren, Jan B. Vermorken, Célia M. Viégas, John Waldron, Klaus‐Dieter Wernecke, Joachim Widder, Gregory T. Wolf, Stuart J. Wong, Jianlin Wu, Hideya Yamazaki, Branko Zaktonik

Bibliographic record

VenueThe Lancet Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
FundersNational Cancer InstituteInstitut National Du CancerECOG-ACRIN Cancer Research GroupMinistère des Affaires Sociales, de la Santé et des Droits des FemmesNRG OncologyFondation ARC pour la Recherche sur le CancerAmerican College of Radiology Imaging Network
KeywordsRadiation therapyHead and neck cancerHead and neckMeta-analysisMedicineOncologyMedical physicsRadiologyInternal medicineSurgery

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.020
metaresearch head score (Gemma)0.038
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.992
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.038
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0080.047
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0030.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0050.001

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 head0.175
GPT teacher head0.421
Teacher spread0.247 · 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.

Study designMeta-analysis
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

Citations107
Published2021
Admission routes1
Has abstractno

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