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Increased High-Density Lipoprotein Levels Associated with Age-Related Macular Degeneration
Johanna M. Colijn, Ayşe Demirkan, Eveline Kersten, Magda A. Meester‐Smoor, B. Merle, Grigorios Papageorgiou, Shahzad Ahmad, Monique T. Mulder, Miguel Costa, Pascale Benlian, Geir Bertelsen, Alain M. Bron, Birte Claes, Catherine Creuzot‐Garcher, Maja Gran Erke, Paul J. Foster, Christopher J. Hammond, Carel B. Hoyng, Anthony P. Khawaja, Jean‐François Korobelnik, Stefano Piermarocchi, Tatiana Segato, Rufino Silva, Eric H. Souied, Katie Williams, Cornelia M. van Duijn, Cécile Delcourt, Caroline C. W. Klaver, Niyazi Acar, Lebriz Altay, Eleftherios Anastosopoulos, Augusto Azuara‐Blanco, Arthur A. Bergen, Christine Binquet, Alan Bird, Martin Bobák, Morten B. Larsen, Camiel J. F. Boon, Rupert Bourne, Lionel Brétillon, Rebecca Broe, Gabriëlle Buitendijk, Maria Luz Cachulo, Vittorio Capuano, Isabelle Carrière, Usha Chakravarthy, Michelle Chan, Petrus Chang, Angela J. Cree, Phillippa Cumberland, José Cunha‐Vaz, Vincent Daïen, Eiko de Jong, Gábor Deák, Marie-Noëlle Delyfer, Anneke Den Hollander, Martha Dietzel, Pedro Faria, Cláudia Farinha, Robert P. Finger, Astrid Fletcher, Panayiota Founti, Theo G. M. F. Gorgels, Jakob Grauslund, Franz Grus, Christopher Hammond, Thomas J. Heesterbeek, Manuel Hermann, René Hoehn, Ruth Hogg, Frank G. Holz, Nomdo M. Jansonius, Sarah Janssen, Jean-François Korobelnik, Julia Lamparter, Mélanie Le Goff, Terho Lehtimäki, Irene Leung, Andrew Lotery, Matthias Mauschitz, Magda Meester, Verena Meyer zu Westrup, Edoardo Midena, Stefania Miotto, Alireza Mirshahi, Sadek Mohan-Saïd, Michael Mueller, Alyson Muldrew, Joaquim Murta, Stefan Nickels, Sandrina Nunes, Christopher G. Owen, Tünde Pető, Norbert Pfeiffer, Elena Prokofyeva, Jugnoo S. Rahi, Olli Raitakari, Franziska G. Rauscher, Luísa Ribeiro, Marie‐Bénédicte Rougier, Alicja R. Rudnicka, José‐Alain Sahel, Aggeliki Salonikiou, Clarisa Sánchez, Tina Schick, Steffen Schmitz‐Valckenberg, Alexander Schuster, Cédric Schweitzer, Jasmin Shehata, Giuliana Silvestri, Christian Simader, Eric Souied, Martynas Špečkauskas, Henriët Springelkamp, Robyn J. Tapp, Fotis Topouzis, Elisa van Leeuwen, Virginie J. M. Verhoeven, Hans Vingerling, Therese von Hanno, Christian Wolfram, Jennifer Yip, Jennyfer Zerbib, Soufiane Ajana, Blanca Arango‐González, Verena Arndt, Vaibhav Bhatia, Shomi S. Bhattacharya, Marc Biarnés, Anna Borrell, Sebastian Bühren, Sofia M. Calado, Sascha Dammeier, Eiko K. de Jong, Berta de la Cerda, Francisco J. Diaz‐Corrales, Sigrid Diether, Eszter Emri, Tanja Endermann, Lucia L. Ferraro, Míriam Garcia, Sabina Honisch, Ellen Kilger, Hanno Langen, Imre Lengyel, Philip J. Luthert, Cyrille Maugeais, Bénédicte M.J. Merle Inserm, Jordi Monés, Everson Nogoceke, Frances M. Pool, Marius Ueffing, Karl Ulrich Bartz‐Schmidt, Timo Verzijden, Markus Zumbansen
Bibliographic record
VenueOphthalmology · 2018
Typearticle
Languageen
FieldMedicine
TopicRetinal Diseases and Treatments
Canadian institutionsnot available
FundersHorizon 2020AllerganMoorfields Eye CharityCaisse nationale de solidarité pour l'autonomieConseil régional de Bourgogne-Franche-ComtéMedical Research CouncilStichting BlindenhulpUniversité de BordeauxNational Institute for Health and Care ResearchUniversité de BourgogneMinisterio de Economía y CompetitividadMoorfields Eye Hospital NHS Foundation TrustNederlandse Organisatie voor Wetenschappelijk OnderzoekQueen's UniversityErasmus Medisch CentrumHorizon 2020 Framework ProgrammeFondation pour la Recherche MédicaleLandelijke Stichting voor Blinden en SlechtziendenRadboud UniversiteitZonMwFondation Voir et EntendreOogfondsInstitut National de la Santé et de la Recherche MédicaleNovartis PharmaEuropean CommissionQueen's University BelfastCentre for Public Health, Queen's University BelfastInstitut National de la Recherche AgronomiqueUniversity College LondonCancer Research UKRadboud Universitair Medisch CentrumFondation de FranceStichting Steunfonds UitzichtMinisterie van Volksgezondheid, Welzijn en SportUniversità degli Studi di PadovaMacula FoundationCentre National de la Recherche ScientifiqueIonis PharmaceuticalsF. Hoffmann-La RocheAgence Nationale de la RechercheNovartis Foundation
KeywordsMedicineMacular degenerationOphthalmology
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)0.000
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 score0.003
Threshold uncertainty score0.009
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 head0.025
GPT teacher head0.283
Teacher spread0.259 · 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
Citations156
Published2018
Admission routes1
Has abstractno