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Record W2778375223 · doi:10.3233/jnd-170281

The Utility of the Laboratory Work Up at the Time of Diagnosis of Amyotrophic Lateral Sclerosis

2017· article· en· W2778375223 on OpenAlexafffundabout
Ario Mirian, Lawrence Korngut

Bibliographic record

VenueJournal of Neuromuscular Diseases · 2017
Typearticle
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsHotchkiss Brain InstituteUniversity of Calgary
FundersHealth Research BoardUniversity of Calgary
KeywordsMedicineAmyotrophic lateral sclerosisSerologyWork-upGold standard (test)Retrospective cohort studyCreatine kinasePhysical examinationPhysical therapyPediatricsInternal medicineDiseaseImmunology

Abstract

fetched live from OpenAlex

BACKGROUND: Serological testing is routinely performed in the work up for a diagnosis of Amyotrophic Lateral Sclerosis (ALS) to exclude pathologies with similar clinical phenotypes. OBJECTIVE: To determine the proportion of serological workup that changes the primary diagnosis and/or clinical management for patients presenting with signs of ALS. METHODS: A retrospective chart review was conducted on patients from the Calgary Neuromuscular Intake Clinic in which the neurologist working diagnosis post-assessment is ALS. Charts from 2012 to 2016 with completed standard serological workup were reviewed. The proportion of abnormal results per investigation was determined and whether it resulted in a change in diagnosis and/or clinical management. RESULTS: A total of 276 charts were reviewed and 85 met full inclusion criteria. Serum creatine kinase (35%), vitamin B12 (18%), complete blood count with differential (11%), and parathyroid hormone (10%) were the among the investigations that had a proportion of abnormal results greater than 5%. Only 6% of patients had an abnormal result that qualified for a change in their clinical management none of which changed the primary diagnosis of ALS. CONCLUSIONS: Standard serological investigations in the work-up for a patient with ALS may have low utility from a diagnostic and management perspective.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.044
Threshold uncertainty score0.532

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.032
GPT teacher head0.282
Teacher spread0.250 · 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

Citations7
Published2017
Admission routes3
Has abstractyes

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