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Immunological and molecular features of the tumor microenvironment of long-term survivors of ovarian cancer
Brad H. Nelson, Phineas T. Hamilton, Minh Tung Phung, Katy Milne, Bronwyn Harris, Shelby Thornton, Donald L.I. Stevens, Shreena Kalaria, Karanvir Singh, Céline M. Laumont, Elena Moss, Aliya Alimujiang, Nicola S. Meagher, Adelyn Bolithon, Sián Fereday, Catherine J. Kennedy, Joy Hendley, Dinuka Ariyaratne, Kathryn Alsop, Nadia Traficante, Ellen L. Goode, Anthony N. Karnezis, Hui Shen, Jean L. Richardson, Cindy McKinnon Deurloo, Anne Chase, Bronwyn Grout, Jennifer A. Doherty, Holly R. Harris, Kara L. Cushing‐Haugen, Michael S. Anglesio, Karolin Heinze, David G. Huntsman, Aline Talhouk, Gillian E. Hanley, Jennifer Alsop, Mercedes Jimenez-Linan, Paul D.P. Pharoah, Jessica Boros, Alison H. Brand, Paul R. Harnett, Raghwa Sharma, Jonathan L. Hecht, Naoko Sasamoto, Kathryn L. Terry, Beth Y. Karlan, Jenny Lester, Michael E. Carney, Marc T. Goodman, Brenda Y. Hernandez, Lynne R. Wilkens, Sabine Behrens, Renée T. Fortner, Peter A. Fasching, Christiani Bisinotto, Francisco José Cândido dos Reis, Prafull Ghatage, Martin Köbel, Esther Elishaev, Francesmary Modugno, Linda S. Cook, Nhu D. Le, Aleksandra Gentry‐Maharaj, Usha Menon, María J. García, Cristina Rodríguez‐Antona, Kyo Farrington, Linda E. Kelemen, Stefan Kommoss, Annette Staebler, Dale W. Garsed, James D. Brenton, Anna Piskorz, David D.L. Bowtell, Anna DeFazio, Susan J. Ramus, Malcolm C. Pike, Celeste Leigh Pearce
Bibliographic record
VenueJournal of Clinical Investigation · 2024
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsWomen's Health Research InstituteBC Cancer AgencyFoothills Medical CentreVancouver General HospitalUniversity of CalgaryUniversity of VictoriaUniversity of British Columbia
FundersMedical Research and Materiel CommandCongressionally Directed Medical Research ProgramsNational Cancer InstituteInstituto de Salud Carlos IIICancer Council TasmaniaCancer Council VictoriaNational Center for Advancing Translational SciencesMedical Research CouncilFoundation MedicineNational Institutes of HealthPeter MacCallum FoundationCanadian Institutes of Health ResearchCancer Institute NSWTerry Fox Research InstituteCancer Council Western AustraliaCancer AustraliaBundesministerium für Bildung und ForschungNational Institute for Health and Care ResearchCancer Council NSWMarshfield Clinic Research FoundationOvarian Cancer AustraliaGenome British ColumbiaFred C. and Katherine B. Andersen FoundationMayo Foundation for Medical Education and ResearchDeutsche ForschungsgemeinschaftVGH and UBC Hospital FoundationFriedrich-Alexander-Universität Erlangen-NürnbergCancer Council South AustraliaDeutsches KrebsforschungszentrumCancer Council QueenslandMinisterio de Economía y CompetitividadUniversity of PittsburghVictorian Cancer AgencyNational Health and Medical Research CouncilAstraZenecaPfizerNational Center for Research ResourcesBC Cancer FoundationOak FoundationCancer Research UKMinnesota Ovarian Cancer AllianceU.S. Department of Defense
KeywordsTerm (time)CancerOvarian cancerTumor microenvironmentCancer researchMedicineOncologyBiologyImmunologyBioinformaticsInternal medicine
Abstract
OpenAlex records an abstract for this work, but it could not be fetched just now.
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)0.000
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 score0.001
Threshold uncertainty score0.003
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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 head0.038
GPT teacher head0.350
Teacher spread0.312 · 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
Citations19
Published2024
Admission routes3
Has abstractyes