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Record W2302632827

Guillain-Barré syndrome--a patient guide and nursing resource.

2001· article· en· W2302632827 on OpenAlexaff
Michael Kehoe

Bibliographic record

VenuePubMed · 2001
Typearticle
Languageen
FieldMedicine
TopicPeripheral Neuropathies and Disorders
Canadian institutionsQueen Elizabeth II Health Sciences Centre
Fundersnot available
KeywordsGuillain-Barre syndromeMedicineWeaknessPsychological interventionParalysisMechanical ventilationIntensive care medicineDiseaseNursing Interventions ClassificationNursingPediatricsSurgeryInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

Guillain-Barré Syndrome (GBS) is an illness characterized by acute neuromuscular paralysis. A review of the history, course of the disease, current treatments, and nursing interventions, as well as excerpts from a patient teaching guide developed by the author for patients with GBS is included in this paper. The objectives are to present information about GBS, first at a level of understanding appropriate for patients and their families, and then to provide a more indepth discussion for health care providers. Despite the potential severity of GBS, the expected outcomes are encouraging. GBS affects 1-2.73 individuals per 100,000/year (Hahn, 1998). The symptoms can range from numbness and tingling with mild weakness to total paralysis requiring mechanical ventilation. Once diagnosed, patients are usually treated with intravenous immune globulin (i.v. IG), which significantly reduces the duration of the illness (Hughes, 1997; Guillain-Barré Syndrome Study Group, 1985). Neuroscience nurses can make a difference in the recovery of their patients by anticipating potential complications and attending to their special needs during the acute and recovery phases of their illness. Aside from physical care, being able to support and teach the patient and family about GBS is crucial. Use of a patient and family teaching guide is one strategy for providing education and support.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.955
Threshold uncertainty score0.384

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.014
GPT teacher head0.226
Teacher spread0.212 · 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 designOther design
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

Citations4
Published2001
Admission routes1
Has abstractyes

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