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Record W4225329224 · doi:10.1186/s13019-022-01849-z

VDI pacing with temporary esophageal and transvenous pacemaker leads to treat post-cardiac surgery cardiogenic shock

2022· article· en· W4225329224 on OpenAlexaff
Sameer Sharif, Adel Dyub, Craig Ainsworth

Bibliographic record

VenueJournal of Cardiothoracic Surgery · 2022
Typearticle
Languageen
FieldMedicine
TopicCardiac pacing and defibrillation studies
Canadian institutionsMcMaster UniversityHamilton General Hospital
Fundersnot available
KeywordsMedicineCardiogenic shockCardiac surgeryCardiologyHeart blockVentricleInternal medicineCardiac pacingConcomitantShock (circulatory)SurgeryElectrocardiographyMyocardial infarction

Abstract

fetched live from OpenAlex

BACKGROUND: Post-operative atrio-ventricular (AV) block after cardiac surgery is not uncommon in high-risk patients. CASE PRESENTATION: Our case highlights the management of a 62-year-old female with cardiogenic shock post-cardiac surgery with concomitant complete heart block. With VVI pacing proving ineffective, it was postulated that the patient may benefit hemodynamically from AV sequential pacing, re-establishing her atrial kick. We describe a novel technique of attaching a temporary pacemaker wire to an orogastric tube to sense atrial p-waves and pace the ventricle transvenously to perform AV sequential pacing. This was done temporarily to stabilize the patient's hemodynamic status while awaiting a permanent pacemaker implantation. CONCLUSIONS: In hemodynamically unstable post-cardiac surgery patients with complete heart block in whom VVI pacing fails to improve their clinical status, clinicians should consider VDI pacing with an orogastric atrial sensing pacemaker lead, in consultation with the cardiac surgeon and the electrophysiology team. Of note, the patient needs to have underlying organized atrial activity for this setup to work.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.240
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.020
GPT teacher head0.268
Teacher spread0.248 · 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.

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

Citations1
Published2022
Admission routes1
Has abstractyes

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