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Record W4247964539 · doi:10.14740/jmc1722w

Using Sugammadex in a Patient With Friedreich Ataxia: A Case Report

2014· article· en· W4247964539 on OpenAlexvenueno aff
Yildirim Guclu

Bibliographic record

VenueJournal of Medical Cases · 2014
Typearticle
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSugammadexAtaxiaAnesthesiaDiseasePediatricsSevofluraneCardiomyopathySurgeryRocuroniumInternal medicinePsychiatryHeart failurePropofol

Abstract

fetched live from OpenAlex

Friedreich ataxia is a rare autosomal recessive disease characterized by demyelination of the nervous system, affecting both genders equally, starting in childhood with muscular coordination deficits that may also cause comorbidities such as cardiomyopathy and diabetes. The disease especially affects anesthesia management in patients planning to receive neuromuscular blockers. We aim to discuss the management of anesthesia technique in such a patient that underwent an appendectomy under general anesthesia. J Med Cases. 2014;5(4):232-233 doi: https://doi.org/10.14740/jmc1722w

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 score0.007
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.002
Science and technology studies0.0030.002
Scholarly communication0.0020.003
Open science0.0020.002
Research integrity0.0070.004
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 head0.061
GPT teacher head0.327
Teacher spread0.266 · 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
Published2014
Admission routes1
Has abstractyes

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Same venueJournal of Medical Cases→Same topicGenetic Neurodegenerative Diseases→French-language works237,207→