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Record W4399035580 · doi:10.1016/j.xjon.2024.05.010

Patient-reported outcome measures after minimally invasive mitral valve surgery: The benefit may be early

2024· article· en· W4399035580 on OpenAlexaff
Amy Brown, Rhys I. Beaudry, Jolene Moen, Sean H. K. Kang, Ali Fatehi Hassanabad, Vishnu Vasanthan, Alexander J. Gregory, William Kent, Corey Adams

Bibliographic record

VenueJTCVS Open · 2024
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsLibin Cardiovascular Institute of AlbertaFoothills Medical CentreUniversity of Calgary
Fundersnot available
KeywordsMedicineMitral valveSurgeryQuality of life (healthcare)Baseline (sea)CardiologyInternal medicine

Abstract

fetched live from OpenAlex

Minimally invasive cardiac surgery is associated with reduced pain, blood loss, transfusions, and hospital length of stay, compared with full median sternotomy (FMS). 1,2long with conventional clinical outcomes, patientreported outcome measures (PROMs) are necessary to inform a patient-centered decision-making model by quantifying patient perspectives. 3ROMs provide a structured approach to define physical, mental, and emotional components of the patient experience and can help determine health-related quality of life (QoL). 1,4The UK Mini Mitral Trial assessed physical functioning and return to usual activities at postoperative week 12 in patients randomized to minimally invasive mitral valve surgery (mini-MVS) or FMS-mitral valve surgery (FMS-MVS).The results showed no difference in mean change in physical function from baseline to 12 weeks, but benefits of minimally invasive approaches may become apparent at earlier time points. 5Observational reports suggest that the benefit of mini-MVS occurs earlier than 12 weeks. 1,4This prospective study aimed to assess health-related QoL defined by PROMs in the early postoperative period for patients who underwent mini-MVS.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.659

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.003
Bibliometrics0.0000.000
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 head0.056
GPT teacher head0.351
Teacher spread0.295 · 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 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

Citations2
Published2024
Admission routes1
Has abstractyes

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