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Record W2111708008 · doi:10.3109/21678421.2014.913635

Diagnostic yield and cost-effectiveness of investigations in patients presenting with isolated lower motor neuron signs

2014· article· en· W2111708008 on OpenAlexaffabout
Mohammed H. Alanazy, Chris White, Lawrence Korngut

Bibliographic record

VenueAmyotrophic Lateral Sclerosis and Frontotemporal Degeneration · 2014
Typearticle
Languageen
FieldMedicine
TopicNeurogenetic and Muscular Disorders Research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineAmyotrophic lateral sclerosisClinical trialRetrospective cohort studyAtrophyLower motor neuronInflammatory myopathyPediatricsSurgeryInternal medicineMyopathyDisease

Abstract

fetched live from OpenAlex

Our objective was to investigate the yield and cost-effectiveness of investigations and therapeutic trials of intravenous immunoglobulin (IVIg) in patients presenting with isolated lower motor neuron (LMN) signs. We performed a retrospective chart review of cases diagnosed between January 2007 and September 2013. Investigation results and their impact on outcome, and outcome of IVIg treatment trials were abstracted. Cost was calculated in Canadian dollars (C$). Fifty-nine of 333 patients presented with isolated LMN signs. The majority of patients (61%) evolved to amyotrophic lateral sclerosis (ALS) within 36 months of presentation, while 37.3% remained with progressive muscular atrophy (PMA) with mean follow-up 29.6 months. Of the 1210 tests performed, 4.9% were abnormal. The diagnosis was changed in only one patient where a muscle biopsy revealed a distal myopathy. Fourteen patients received therapeutic trials of IVIg to rule out an IVIg-responsive inflammatory motor neuropathy with no objective clinical benefit. Total group cost was C$630,484.72 (C$10,686.18/patient). IVIg represented 58.7% of total costs. In conclusion, extensive investigations and treatment trials of IVIg have low yield in the work-up of patients with isolated LMN signs and are not cost-effective when clinical features do not suggest an alternative diagnosis to PMA.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score0.429

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 head0.030
GPT teacher head0.243
Teacher spread0.214 · 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 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

Citations5
Published2014
Admission routes2
Has abstractyes

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