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Chemotherapy and radiotherapy in locally advanced head and neck cancer: an individual patient data network meta-analysis
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
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.702
Threshold uncertainty score0.697
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 head0.175
GPT teacher head0.421
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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