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Record W4389752146 · doi:10.1186/s13256-023-04294-1

Emotional dysmetria after cerebellar-pontine stroke: a case report

2023· article· en· W4389752146 on OpenAlexaff
Rebecca Long, Michèle G. DuVal, Bridget Mulvany-Robbins, Amanda N. Wagner, Glen C. Jickling

Bibliographic record

VenueJournal of Medical Case Reports · 2023
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsUniversity of AlbertaWomen and Children’s Health Research InstituteUniversity of Calgary
Fundersnot available
KeywordsDysmetriaPseudobulbar palsyDysarthriaMedicineStroke (engine)Physical medicine and rehabilitationCerebellumPsychologyPsychiatryInternal medicineSurgery

Abstract

fetched live from OpenAlex

INTRODUCTION: Pseudobulbar affect, or emotional dysregulation, commonly occurs following stroke. However, it is frequently missed in cases involving the cerebellum, resulting in a lack of treatment, which can directly impact stroke rehabilitation. CASE PRESENTATION: A 63-year-old Caucasian female with no history of mood disorders presented with gait instability, dysarthria, and right sided hemiplegia, secondary to cerebellar and pontine ischemic stroke from a basilar occlusion. She underwent endovascular therapy and her deficits gradually improved. However during recovery she began to develop uncontrollable tearfulness while retaining insight that her emotional expression was contextually inappropriate. She was treated with a selective serotonin reuptake inhibitor with reported improvements in her emotional regulation at one year follow up. CONCLUSION: This case highlights cerebellar injury as a potential cause of poorly regulated emotions, or an emotional dysmetria. The recognition of this disorder in patients with cerebellar or pontine strokes is critical, as untreated pseudobulbar affect can impact future stroke rehabilitation.

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)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.003
Science and technology studies0.0040.003
Scholarly communication0.0030.003
Open science0.0020.003
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.019
GPT teacher head0.326
Teacher spread0.307 · 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 designCase report
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
Published2023
Admission routes1
Has abstractyes

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