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Record W2166691293 · doi:10.1136/jnnp.2008.161059

The effectiveness of long-term dietary therapy in the treatment of adult Refsum disease

2010· article· en· W2166691293 on OpenAlexaff
Eleanor Baldwin, F B Gibberd, Carolyn Harley, M C Sidey, Michael Feher, Anthony S. Wierzbicki

Bibliographic record

VenueJournal of Neurology Neurosurgery & Psychiatry · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and Kidney Cyst Diseases
Canadian institutionsSt. Thomas Hospital
Fundersnot available
KeywordsPhytanic acidMedicineInternal medicinePediatricsRetrospective cohort studyDiseasePlasmapheresisTyrosinemiaSurgeryBiochemistryImmunology

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate the long-term effectiveness of dietary therapy with regular dietetic reinforcement for adult Refsum disease. METHODS: Retrospective case note analysis of records of plasma phytanic acid and hospital admission of 13 patients with adult Refsum disease who attended the specialist centre and repeatedly received dietary instruction for a minimum of 10 years. RESULTS: Patients undergoing review had attended for 11-28 years totalling 237 years. Median baseline phytanic acid concentrations at presentation were 1631 (370-2911) micromol/l and declined by 89+/-11% to 85 (10-1325) micromol/l. Levels of phytanic acid were completely normalised (<30 micromol/l) in 30%; partially normalised (30-300 micromol/l) in 50% and remained >300 pmol/l in 15%. The time required for phytanic acid levels to halve was 44.2+/-15.9 months in patients compliant with diet. No patient required admission or plasmapheresis/apheresis during this period for acute neuro-ophthalmological complications despite occasional spikes in phytanic acid levels attributable to intercurrent illness, surgery, sudden weight loss or psychological illness. INTERPRETATION: Dietary modification with regular reinforcement in Adult Refsum Disease can significantly reduce phytanic acid levels with time.

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 score0.189
Threshold uncertainty score0.415

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 head0.008
GPT teacher head0.258
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

Citations84
Published2010
Admission routes1
Has abstractyes

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