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Record W3093985136 · doi:10.1093/pm/pnaa371

Navigating the Gray Zone—Motor Weakness Due to Noncompressive Neuropathy: Experience at a Single Pain Clinic

2020· article· en· W3093985136 on OpenAlexaff
Min Cheol Chang, Mathieu Boudier‐Revéret

Bibliographic record

VenuePain Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicPeripheral Neuropathies and Disorders
Canadian institutionsCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsMedicineWeaknessPhysical medicine and rehabilitationEntrapment NeuropathyGray (unit)Neuropathic painPhysical therapyAnesthesiaSurgeryNuclear medicine

Abstract

fetched live from OpenAlex

Dear Editor, At pain clinics, physicians frequently encounter patients with motor weakness, for which a common etiology is compressive neuropathy, including herniated intervertebral disc, spinal stenosis, carpal tunnel syndrome, and ulnar neuropathy at the elbow. However, in clinical practice, there are several noncompressive neural disorders causing weakness, and without a detailed examination, they are often indistinguishable from compressive neural disorders. Also, many pain specialists may be less clinically aware of these noncompressive neural disorders. Therefore, some cases of noncompressive neural disorders may be undiagnosed or misdiagnosed and may receive inappropriate treatment. For example, multifocal acquired demyelinating sensory and motor (MADSAM) neuropathy or multifocal motor neuropathy can be misdiagnosed as an entrapment of peripheral nerves and treated with corticosteroid injection on peripheral nerves. Cord infarct of the conus medullaris and early-stage amyotrophic lateral sclerosis can be confused with herniated lumbar disc and treated with epidural steroid injection. In addition to the above examples, pain physicians may experience other similar cases in their clinical practice. Because nerve compressions revealed on imaging can be asymptomatic, and compressive and noncompressive neural disorders sometimes have features similar to motor weakness, misdiagnosis can occasionally occur.

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)0.002
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.640
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.304
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations2
Published2020
Admission routes1
Has abstractyes

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