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The “Woundosome” Concept and Its Impact on Procedural Outcomes in Patients With Chronic Limb-Threatening Ischemia
Lorenzo Patrone, Edoardo Pasqui, Michael S. Conte, Alik Farber, Roberto Ferraresi, Matthew T. Menard, Joseph L. Mills, John H. Rundback, Peter A. Schneider, August Ysa, Kumar Abhishek, George L. Adams, Naseer Ahmad, Irfan Ahmed, Vlad-Adrian Alexandrescu, Max Amor, David P. Alper, Martin Andrassy, Christopher E. Attinger, Andy Baadh, Hashem Barakat, Lukla Biasi, Theodosios Bisdas, Zagum Bhatti, Erwin Blessing, Marc P. Bonaca, Stefano Bonvini, Michel Bosiers, Andrew W. Bradbury, Robert Beasley, Christian‐Alexander Behrendt, Marianne Brodmann, Gonzalo Cabral, Roberto Cancellieri, Andrea Casini, Venita Chandra, Emiliano Chisci, Omar Chohan, Edward Choke, Patrick Chong, Giacomo Clerici, Raphaël Coscas, Mary Costantino, Luca Dalla Paola, Sabeen Dand, Robert Davies, Mario D’Oria, Αθανάσιος Διαμαντόπουλος, Sebastian Debus, Koen Deloose, Costantino Del Giudice, Gianmarco de Donato, Brian De Rubertis, Jean Paul de Vries, Nuno Dias, Larry J. Díaz‐Sandoval, Florian Dick, Konstantinos P. Donas, Anahita Dua, Fabrizio Fanelli, Stefano Fazzini, Mazin I. Foteh, Roberto Gandini, Mauro Gargiulo, Luca Garriboli, Elizabeth Genovese, Edward Gifford, Yann Gouëffic, Peter Goverde, Prem Chand Gupta, Robert J. Hinchliffe, Andrew Holden, Kim Christian Houlind, D. Howard, Bella Huasen, Giacomo Isernia, Konstantinos Katsanos, Barry T. Katzen, Philippe Kolh, Igor Končar, Grigorios Korosoglou, Prakash Krishnan, Thomas Kroencke, Μiltiadis Krokidis, Arun Kumarasamy, Paul D. Hayes, Osamu Iida, Enrique Alejandre Lafont, Ralf Langhoff, Alexandre Lecis, Mark L. Lessne, Hady Lichaa, Michael Lichtenberg, Marta Lobato, A. de M. Lopes, Giorgio Loreni, Pierleone Lucatelli, Sreekumar Madassery, Lieven Maene, Marco Manzi, Martin Maresch, Jay Mathews, James McCaslin, Antonio Micari, Stefano Michelagnoli, Bruno Migliara, Robert Morgan, Luis G. Morelli, Daniele Morosetti, Nicolas J. Mouawad, Paul Moxey, Stefan Müller‐Hülsbeck, Jihad Mustapha, Tatsuya Nakama, Bahaa Nasr, Zola N’Dandu, Richard F. Neville, Elias Noory, Joakim Nordanstig, Katariina Noronen, Luis Mariano Palena, Gianbattista Parlani, Ashish Patel, Parag J. Patel, Rafiuddin Patel, Sanjay Patel, Costantino Pena, Dražen Perkov, Mark Portou, Giovanni Pratesi, Christos Rammos, Jim A. Reekers, Vicente Riambau, Trisha Roy, Kenneth Rosenfield, Maria Antonella Ruffino, Fadi Saab, Athanasios Saratzis, Paolo Sbarzaglia, Andrej Schmidt, Eric A. Secemsky, Michael Siah, Henrik Sillesen, Gioele Simonte, Marc Sirvent, Jill Sommerset, Sabine Steiner, Ahmed Youssef Sakr, Dierk Scheinert, Mehdi Shishebor, Stavros Spiliοpoulos, Alessio Spinelli, Konstantinos Stravoulakis, Gergana T. Taneva, Desarom Teso, Joerg Teßarek, Selva Theivacumar, Anish Thomas, Shannon D. Thomas, Narayanan Thulasidasan, Giovanni Torsello, Ramesh K. Tripathi, Nicola Troisi, Srini Tummala, Venkat Tummala, Christopher P. Twine, Raman Uberoi, Alessandro Ucci, Domenico Valenti, Jos Van Den Berg, Daniel van den Heuvel, Isabelle Van Herzeele, Ramon L. Varcoe, Melina Vega de Céniga, Frank J. Veith, Maarit Venermo, Badri Vijaynagar, Sanjiv Virdee, Conrad von Stempel, Michiel T. Voûte, Kak Khee Yeung, Thomas Zeller, Hany Zayed, Miguel Montero Baker
Bibliographic record
VenueJournal of Endovascular Therapy · 2024
Typeeditorial
Languageen
FieldMedicine
TopicPeripheral Artery Disease Management
Canadian institutionsSt. Thomas Hospital
FundersAcademy of Medical SciencesNational Heart, Lung, and Blood InstituteNational Institute for Health and Care Research
KeywordsMedicineLimb ischemiaCritical limb ischemiaIschemiaCardiologyRevascularizationInternal medicineIntensive care medicineMyocardial infarction
Abstract
N/A
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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)0.006
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.014
Threshold uncertainty score0.032
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.014 | 0.020 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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 head0.006
GPT teacher head0.276
Teacher spread0.270 · 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 designNot applicable
Domainnot available
GenreEditorial
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
Citations24
Published2024
Admission routes1
Has abstractyes