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Record W4380989652 · doi:10.18535/jmscr/v11i4.06

Peripheral Nerve Blocks as Anaesthetic Choice for Asymptomatic Severe Aortic Stenosis for Emergency Lower Limb Surgeries: A Case Report

2023· article· en· W4380989652 on OpenAlexaff
Anu Ambooken

Bibliographic record

VenueJournal of Medical Science And clinical Research · 2023
Typearticle
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsSt. Thomas Hospital
Fundersnot available
KeywordsMedicineAsymptomaticPeripheralPeripheral nerveStenosisAnesthesiaSurgeryCardiologyInternal medicineAnatomy

Abstract

fetched live from OpenAlex

Patients with severe aortic stenosis, whether symptomatic or not possess unique challenge to the anaesthetist .Both general anaesthesia and regional anaesthesia with central neuraxial blockade carry potential risks owing to a fixed cardiac output.Haemodynamic fluctuations associated with anaesthesia can be detrimental in these patients.They are at increased risk for intraoperative and post-operative complications given the severity of aortic stenosis.A clear intraoperative plan should be designed to manage the unique haemodynamics of these patients.We report a case of assymptomatic severe aortic stenosis posted for an urgent lower limb surgery.Ultra sound guided combined popliteal-sciatic and femoral nerve blocks were used as a sole anaesthetic technique.Patient was haemodynamically stable in the intra-operative as well as post operative period.Patient did not require any additional analgesic for about 10 hours post-operatively.

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 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: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.143
GPT teacher head0.514
Teacher spread0.371 · 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 designCase report
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

Citations0
Published2023
Admission routes1
Has abstractyes

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