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Survival of patients with skin melanoma in Europe increases further: Results of the EUROCARE-5 study
Emanuele Crocetti, Sandra Mallone, Trude Eid Robsahm, Anna Gavin, Domenic Agius, Eva Ardanáz, María‐Dolores Chirlaque López, Kaire Innos, Pamela Minicozzi, Lorenzo Borgognoni, Daniela Pierannunzio, Nora Eisemann, Monika Hackl, N. Zielonke, Willi Oberaigner, E. Van Eycken, K. Henau, Z. Valerianova, Nadya Dimitrova, Mario Šekerija, Miroslav Zvolský, Ladislav Dušek, Hans H. Storm, Gerda Engholm, Margit Mägi, Tiiu Aareleid, Nea Malila, Karri Seppä, Michel Velten, X. Troussard, Véronique Bouvier, Guy Launoy, A.V. Guizard, Jean Faivre, Anne‐Marie Bouvier, P. Arveux, Marc Maynadié, Anne‐Sophie Woronoff, Michel Robaszkiewicz, Isabelle Baldi, Alain Monnereau, Brigitte Trétarre, Nadine Bossard, Aurélien Belot, Marc Colonna, Florence Molinié, S. Bara, Claire Schvartz, B. Lapôtre‐Ledoux, Pascale Grosclaude, M. Meyer, Roland Stabenow, Sabine Luttmann, Andrea Eberle, Hermann Brenner, Alice Nennecke, Jutta Engel, G. Schubert- Fritschle, Joachim Kieschke, Jan Heidrich, Bernd Holleczek, Alexander Katalinic, Jón G. Jónasson, Laufey Tryggvadóttír, Harry Comber, Guido Mazzoleni, A. Bulatko, Carlotta Buzzoni, Adriano Giacomin, A. Sutera Sardo, Pamela Mancuso, Stefano Ferretti, Adele Caldarella, Gianfranco Manneschi, Gemma Gatta, Milena Sant, H. Amash, C. Amati, Paolo Baili, Franco Berrino, S. Bonfarnuzzo, Laura Botta, F. Di Salvo, Roberto Foschi, C. Margutti, E. Meneghini, Annalisa Trama, Diego Serraino, Luigino Dal Maso, Roberta De Angelis, M. Caldora, Riccardo Capocaccia, E. Carrani, Silvia Francisci, P. Roazzi, Silvia Rossi, Santaquilani, Andrea Tavilla, Fabio Pannozzo, M. Natali, L Bonelli, Marina Vercelli, Valerio Gennaro, Paolo Ricci, M. Autelitano, Giorgia Randi, Maurizio Ponz de Leòn, C. Marchesi, Claudia Cirilli, Mario Fusco, M.F. Vitale, M. Usala, A. Traina, R. Staiti, Francesco Vitale, Barbara Ravazzolo, Maria Michiara, Rosario Tumino, Paolo Giorgi Rossi, E. Di Felice, Fabio Falcini, Antonio Iannelli, O. Sechi, R. Cesaraccio, Silvano Piffer, A Madeddu, F. Tisano, S. Maspero, A.C. Fanetti, Roberto Zanetti, Stefano Rosso, P. Candela, T. Scuderi, Fabrizio Stracci, Fortunato Bianconi, Giovanna Tagliabue, Paolo Contiero, Angelo Paolo Dei Tos, Stefano Guzzinati, Santa Pildava, Giedrė Smailytė, Calleja, D. Agius, T.B. Johannesen, Jadwiga Rachtan, Jerzy Błaszczyk, Marek Bębenek, Magdalena Bielska‐Lasota, G. Forjaz de Lacerda, María José Bento, Clara Castro, A. Miranda, A. Mayer-da-Silva, Florian Nicula, Daniela Coza, C. Safaei Diba, Maja Primic‐Žakelj, E. Almar, C. Ramírez, M. Errezola, J. Bidaurrazaga, A. Torrella-Ramos, J.M. Díaz García, R. Jimenez-Chillaron, Rafael Marcos‐Gragera, María‐José Sánchez, D.Y.L. Chang, Carmen Navarro, María‐Dolores Chirlaque, C. Moreno-Iribas, Jaume Galcerán, M. Carulla, Mats Lambe, S. Khan, Seyed Mohsen Mousavi, Christine Bouchardy, M. Usel, S.M. Ess, H. Frick, Matthias Lorez, C. Herrmann, Andrea Bordoni, Alessandra Spitale, Isabelle Konzelmann, Otto Visser, V.E.P.P. Lemmens, Michel P. Coleman, Claudia Allemani, Bernard Rachet, Julia Verne, N. Easey, Gill Lawrence, Teresa Morán, Jem Rashbass, M. Roche, J. Wilkinson, David Donnelly, David Brewster, Dyfed Huws, Peter D. White, R. Otter
Bibliographic record
VenueEuropean Journal of Cancer · 2015
Typearticle
Languageen
FieldMedicine
TopicCutaneous Melanoma Detection and Management
Canadian institutionsnot available
FundersAction Against CancerMinistero della SaluteFondazione CariploQueen's University BelfastQueen's UniversityEuropean CommissionCompagnia di San Paolo
KeywordsOverdiagnosisDemographyMedicineConfidence intervalRelative survivalSurvival analysisInternal medicineCancerCancer registry
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)0.001
metaresearch head score (Gemma)0.002
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 score0.005
Threshold uncertainty score0.011
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 head0.018
GPT teacher head0.252
Teacher spread0.233 · 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
Citations112
Published2015
Admission routes1
Has abstractno