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The mental health impact of aortic dissection

2022· review· en· W4212852763 on OpenAlexaff
Nicole Ilonzo, Ella Taubenfeld, Maisoon D. Yousif, Claudine Henoud, Jake Howitt, Max V. Wohlauer, Mario D’Oria, Gretchen MacCarrick, Melanie Case, Novelett E. Cotter, Carmen C. David, Mark Fasano, Richard Goldenberg, Timo T. Söderlund, Debra Trotter, Asaf Rabin, Mattie Boehler-Tatman, Melissa L. Russo, Laura M. Drudi, Laura L. Marks, Tabea Hoffstaetter, Sreekanth Vemulapalli, Chrisanne S. Campos, Lindsey Rusche, Robert C.F. Pena, Firas F. Mussa, Earl Goldsborough, Christeen Samuel, Lillian Xu, Nicolas J. Mouawad, Eanas S. Yassa, Xiaoyi Teng, Amani D. Politano, Jesse Teindl, Lara Bloom, Rebecca Gluck, Meredith Ford O'Neal, Josephine Grima, Eileen Masciale, Takeyoshi Ota, Katelyn Wright, Alan J. Hakim, Gareth Owens, George J. Arnaoutakis, Dejah R. Judelson, Lurdes del Rio-Sola, Mark Ajalat, Marvin Chau, Stephanie D. Talutis, Karen Woo, JeniannA. Yi, Kim A. Eagle, Marion A. Hofmann Bowman, Eva Kline‐Rogers, Hyein Kim, Scott M. Damrauer, Emilia Król, Rana O. Afifi, Alana C. Cecchi, Madeline Drake, Anthony L. Estrera, Avery M Hebert, Dianna M. Milewicz, Siddharth K. Prakash, Aaron W. Roberts, Harleen K. Sandhu, Akili Smith-Washington, Akiko Tanaka, Jacob Watson, Myra Ahmad, Catherine M. Albright, Christopher R. Burke, Peter H. Byers, L’Oreal Kennedy, Sarah Lawrence, Jenney R. Lee, Jonathan Medina, Thamanna Nishath, Julie Pham, Courtney Segal, Sherene Shalhub, Michael Soto, Linell Catalan, Megan Patterson

Bibliographic record

VenueSeminars in Vascular Surgery · 2022
Typereview
Languageen
FieldMedicine
TopicAortic Disease and Treatment Approaches
Canadian institutionsCentre Hospitalier de l’Université de MontréalUniversity of OttawaDalhousie University
Fundersnot available
KeywordsMedicineMental healthAortic dissectionExistentialismEvent (particle physics)PsychiatrySurgeryAorta

Abstract

fetched live from OpenAlex

Although the topics of surgical techniques and medical therapies have been widely studied in aortic dissection (AD), studies examining the short- and long-term impact of this event on mental health are largely lacking. Many of the studies have used the 36-Item Short Form Survey at variable time points after the event. However, AD as medical trauma has a complex impact on a person's identity. Its implications for emotional, mental, social, existential, and self-concept could be explored more robustly. This topic was identified as a topic of interest by the AD Collaborative. The AD Collaborative Mental Health Working Group was formed and performed a landscape review to summarize current literature surrounding quality of life research in those who have experienced AD, identify gaps in knowledge, and to outline future research questions.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.988
Threshold uncertainty score0.723

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.050
GPT teacher head0.360
Teacher spread0.310 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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

Citations18
Published2022
Admission routes1
Has abstractyes

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