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Incidence and Risk Factors Related to Symptomatic Venous Thromboembolic Events After Esophagectomy for Cancer

2016· article· en· W2418082941 on OpenAlexaff
Styliani Mantziari, Caroline Gronnier, Arnaud Pasquer, Johan Gagnière, Jérémie Thereaux, Nicolas Demartines, Markus Schäfer, C. Mariette, Abdennahceur Dhahri, Delphine Lignier, Cyril Cossé, Jean‐Marc Regimbeau, Guillaume Luc, Denis Collet, Magalie Cabau, Jacques Jougon, Bogdan Badic, P Lozac’h, Jean Pierre Bail, Serge Cappeliez, Issam El Nakadi, Gil Lebreton, Arnaud Alvès, Renaud Flamein, Denis Pezet, Federica Pipitone, Bogdan Stan-Iuga, Xaviéra Coueffé, Nicolas Contival, Eric Pappalardo, Simon Msika, Flora Hec, Marguerite Vanderbeken, Williams Tessier, Nicolas Briez, Fabien Fredon, A Gainant, Muriel Mathonnet, Jean‐Marc Bigourdan, Salim Mezoughi, Christian Ducerf, J Baulieux, Jean‐Yves Mabrut, Oussama Baraket, Gilles Poncet, Mustapha Adam, Delphine Vaudoyer, Peggy Jourdan Enfer, Laurent Villeneuve, Olivier Gléhen, Thibault Coste, Jean-Michel Fabre, Frédéric Marchal, Romain Frisoni, Ahmet Ayav, Laurent Brunaud, Laurent Bresler, Charlotte Cohen, Olivier Aze, Nicolas Vénissac, Daniel Pop, Jérôme Mouroux, Ion Donici, Michel Prudhomme, Emanuele Felli, Stéphanie Lisunfui, M. Seman, Gaëlle Petit, Mehdi Karoui, Christophe Trésallet, F. Ménégaux, Jean‐Christophe Vaillant, L Hannoun, Brice Malgras, Denis Lantuas, Karine Pautrat, Marc Pocard, Patrice Valleur, Jérémie H. Lefèvre, Najim Chafaı̈, Pierre Balladur, Magalie Lefrançois, Yann Parc, François Paye, Emmanuel Tiret, Marius Nedelcu, Letizia Laface, T Perniceni, Brice Gayet, Kathleen Turner, Bernard Meunier, Alexandre Filipello, Jack Porcheron, Olivier Tiffet, Noémie Kamlet, Rodrigue Chemaly, Amandine Klipfel, Patrick Pessaux, Cécile Brigand, S. Rohr, Mael Chalret du Rieu, Nicolas Carrère, Chiara Da Re, Frédéric Dumont, Diane Goèré, Dominique Elias, Claude Bertrand

Bibliographic record

VenueThe Annals of Thoracic Surgery · 2016
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsMontreal Heart Institute
Fundersnot available
KeywordsMedicineIncidence (geometry)Pulmonary embolismARDSEsophagectomyLow molecular weight heparinVenous thrombosisEsophageal cancerVenous thromboembolismThrombosisSurgeryCancerInternal medicineLung

Abstract

fetched live from OpenAlex
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 imitation

Not 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)0.003
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.051
GPT teacher head0.370
Teacher spread0.319 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine 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

Citations30
Published2016
Admission routes1
Has abstractno

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