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Record W4213369447 · doi:10.3389/fneur.2021.788168

Adult-Onset Leukoencephalopathy With Axonal Spheroids and Pigmented Glia: Review of Clinical Manifestations as Foundations for Therapeutic Development

2022· review· en· W4213369447 on OpenAlexaff
Spyros Papapetropoulos, Angela Pontius, Elizabeth Finger, Virginija Danylaité Karrenbauer, David S. Lynch, Matthew Brennan, Samantha Zappia, Wolfgang Koehler, Ludger Schoels, Stefanie N. Hayer, Takuya Konno, Takeshi Ikeuchi, Troy C. Lund, Jennifer Orthmann‐Murphy, Florian Eichler, Zbigniew K. Wszołek

Bibliographic record

VenueFrontiers in Neurology · 2022
Typereview
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsWestern University
FundersNational Institute of Neurological Disorders and Stroke
KeywordsMedicineNeuropathologyClinical trialLeukoencephalopathyDiseaseIntensive care medicineLeukodystrophyNeurosciencePathologyBioinformaticsPsychology

Abstract

fetched live from OpenAlex

A comprehensive review of published literature was conducted to elucidate the genetics, neuropathology, imaging findings, prevalence, clinical course, diagnosis/clinical evaluation, potential biomarkers, and current and proposed treatments for adult-onset leukoencephalopathy with axonal spheroids and pigmented glia (ALSP), a rare, debilitating, and life-threatening neurodegenerative disorder for which disease-modifying therapies are not currently available. Details on potential efficacy endpoints for future interventional clinical trials in patients with ALSP and data related to the burden of the disease on patients and caregivers were also reviewed. The information in this position paper lays a foundation to establish an effective clinical rationale and address the clinical gaps for creation of a robust strategy to develop therapeutic agents for ALSP, as well as design future clinical trials, that have clinically meaningful and convergent endpoints.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.117
GPT teacher head0.389
Teacher spread0.272 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations80
Published2022
Admission routes1
Has abstractyes

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