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Record W1825766421 · doi:10.4187/respcare.06511016

Neuromuscular Disease Causing Acute Respiratory Failure

2006· article· en· W1825766421 on OpenAlexaff
Sangeeta Mehta

Bibliographic record

VenueRespiratory Care · 2006
Typearticle
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsMount Sinai Hospital
Fundersnot available
KeywordsMedicineMechanical ventilationMyasthenia gravisRespiratory failureNeuromuscular diseaseWeaknessVentilation (architecture)Muscle weaknessIntensive care unitGuillain-Barre syndromeIntubationNeuromuscular transmissionIntensive care medicinePulmonary function testingRespiratory systemIntensive careAnesthesiaDiseaseSurgeryInternal medicinePediatrics

Abstract

fetched live from OpenAlex

In the developed world, Guillain-Barré syndrome and myasthenia gravis account for the majority of cases of acute respiratory failure associated with neuromuscular disease. The 4 components that contribute to respiratory failure are upper-airway dysfunction, inspiratory-muscle weakness, expiratory-muscle weakness, and the pulmonary complications associated with these conditions. Careful observation and objective monitoring are essential to determine the appropriate timing of intubation and mechanical ventilation. Pulmonary function tests that can help predict the need for mechanical ventilation include vital capacity, peak inspiratory pressure, and peak expiratory pressure. The morbidity and mortality of patients who require mechanical ventilation are not insubstantial. This paper will review the mechanisms underlying acute respiratory failure, the clinical assessment of patients, the predictors of the need for mechanical ventilation, and the intensive-care-unit morbidity and mortality of patients with Guillain-Barré syndrome or myasthenia gravis.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.015
GPT teacher head0.267
Teacher spread0.252 · 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

Citations257
Published2006
Admission routes1
Has abstractyes

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