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Clinical impact of COVID-19 on patients with cancer (CCC19): a cohort study
Nicole M. Kuderer, Toni K. Choueiri, Dimpy P. Shah, Yu Shyr, Samuel M. Rubinstein, Donna R. Rivera, Sanjay Shete, Chih–Yuan Hsu, Aakash Desai, Gilberto Lopes, Petros Grivas, Corrie Painter, Solange Peters, Ziad Bakouny, Gerald Batist, Tanios Bekaii‐Saab, Mehmet Asım Bilen, Nathaniel Bouganim, Mateo Bover Larroya, Daniel Castellano, Salvatore A. Del Prete, Deborah B. Doroshow, Pamela Egan, Arielle Elkrief, Dimitrios Farmakiotis, Daniel Flora, Matthew D. Galsky, Michael Glover, Elizabeth A. Griffiths, Anthony P. Gulati, Shilpa Gupta, Navid Hafez, Þorvarður R. Hálfdánarson, Jessica E. Hawley, Emily Hsu, Anup Kasi, Ali Raza Khaki, Christopher A. Lemmon, Colleen Lewis, Barbara Logan, Tyler Masters, Rana R. McKay, Ruben A. Mesa, Alicia K. Morgans, Mary F. Mulcahy, Orestis A. Panagiotou, Prakash Peddi, Nathan A. Pennell, Kerry L. Reynolds, L Rosen, Rachel Rosovsky, Mary Salazar, Andrew Schmidt, Sumit Shah, Justin Shaya, John A. Steinharter, Keith Stockerl‐Goldstein, Suki Subbiah, Donald C. Vinh, Firas Wehbe, Lisa B. Weissmann, Julie Wu, Elizabeth Wulff‐Burchfield, Zhuoer Xie, Albert C. Yeh, Peter Paul Yu, Alice Y. Zhou, Leyre Zubiri, Sanjay Mishra, Gary H. Lyman, Brian I. Rini, Jeremy L. Warner, Maheen Z. Abidi, Jared D. Acoba, Neeraj Agarwal, Syed A. Ahmad, Archana Ajmera, Jessica K. Altman, Anne H. Angevine, Nilo Azad, Michael Bär, Aditya Bardia, Jill S. Barnholtz‐Sloan, Briana Barrow, Babar Bashir, Rimma Belenkaya, Stephanie Berg, Eric Bernicker, Christine M. Bestvina, Rohit Bishnoi, Genevieve M. Boland, Mark Bonnen, Gabrielle Bouchard, Daniel W. Bowles, Fiona Busser, Angelo Cabal, Paolo F. Caimi, Theresa M. Carducci, Carla Casulo, James L. Chen, Jessica Clément, David D. Chism, Erin Cook, Catherine Curran, Ahmad Daher, Mark Dailey, Saurabh Dahiya, John F. Deeken, George D. Demetri, Sandy DiLullo, Narjust Duma, Rawad Elias, Bryan A. Faller, Leslie A. Fecher, Lawrence Feldman, Christopher R. Friese, Paul Fu, Julie Fu, Andy Futreal, Justin F. Gainor, Jorge Garcia, David Gill, Erin A. Gillaspie, Antonio Giordano, Grace Glace, Axel Grothey, Shuchi Gulati, Michael Gurley, Balázs Halmos, Roy S. Herbst, Dawn L. Hershman, Kent Hoskins, Rohit Jain, Salma K. Jabbour, Alokkumar Jha, Douglas B. Johnson, Monika Joshi, Kaitlin M. Kelleher, Jordan Kharofa, Hina Khan, Jeanna Knoble, Vadim S. Koshkin, Amit Kulkarni, Philip E. Lammers, John Leighton, Mark A. Lewis, Xuanyi Li, Ang Li, K. M. Steve Lo, Arturo Loaiza‐Bonilla, Patricia LoRusso, Clarke A. Low, Maryam B. Lustberg, Daruka Mahadevan, Abdul-Hai Mansoor, Michelle Marcum, Merry Jennifer Markham, Catherine H. Marshall, Sandeep H. Mashru, Sara Matar, Christopher McNair, Shannon K. McWeeney, Janice M. Mehnert, Alvaro G. Menendez, Harry Menon, Marcus Messmer, Ryan Monahan, Sarah Mushtaq, Gayathri Nagaraj, Sarah Nagle, Jarushka Naidoo, John Nakayama, Vikram M. Narayan, Heather H. Nelson, Eneida R. Nemecek, Pier Vitale Nuzzo, Paul E. Oberstein, Adam J. Olszewski, Susie Owenby, Mary Pasquinelli, John Philip, Sabitha Prabhakaran, Matthew Puc, Amelie G. Ramírez, Joerg Rathmann, Sanjay G. Revankar, Young Soo Rho, Terence D. Rhodes, Robert Rice, Gregory J. Riely, Jonathan W. Riess, Cameron Rink, Elizabeth Robilotti, Lori J. Rosenstein, Bertrand Routy, Marc A. Rovito, Muhammad Wasif Saif, Amit Sanyal, Lidia Schapira, Candice Schwartz, Oscar K. Serrano, Mansi Shah, Chintan Shah, Grace Shaw, Ardaman Shergill, Geoffrey Shouse, Heloisa P. Soares, Carmen C. Solórzano, Pramod K. Srivastava, Karen Stauffer, Daniel G. Stover, Jamie Stratton, Catherine Stratton, Vivek Subbiah, Rulla M. Tamimi, Nizar M. Tannir, Ümit Topaloĝlu, Eli Van Allen, Susan Van Loon, Karen Vega-Luna, Neeta K. Venepalli, Amit Verma, Praveen Vikas, Sarah Wall, Paul L. Weinstein, Matthias Weiss, Trisha M. Wise‐Draper, William A. Wood, Wenxin Xu, Susan Yackzan, Rosemary Zacks, Tian Zhang, Andrea Zimmer, Jack West
Bibliographic record
VenueThe Lancet · 2020
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsMcGill University Health CentreMcGill UniversityJewish General Hospital
FundersNational Center for Advancing Translational SciencesNational Human Genome Research InstituteHope FoundationVanderbilt Institute for Clinical and Translational ResearchNational Cancer InstituteNational Institutes of HealthVanderbilt UniversityAmerican Cancer Society
KeywordsMedicineInternal medicineCohortCancerCohort studyLogistic regressionProstate cancerMalignancyBreast cancer
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 machine prediction
Teacher imitationNot 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)0.009
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 score0.032
Threshold uncertainty score0.063
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 head0.196
GPT teacher head0.509
Teacher spread0.313 · 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 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
Citations1,802
Published2020
Admission routes1
Has abstractno