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Record W6968444371 · doi:10.5281/zenodo.14678453

Pathological Laughing and Crying Post-stroke: Liaison Psychiatrist Beware

2016· article· en· W6968444371 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2016
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsCryingPathologicalStimulus (psychology)LaughterEmpathyEmotional disorder

Abstract

fetched live from OpenAlex

Pathological laughing and crying (PLC) has been known by a number of different names, but the most widely used terms are “pseudobulbar affect,” “emotional lability,” “emotional incontinence,” and “pathological laughing and crying.” The cardinal feature of PLC is a pathologically lowered threshold for the exhibition of behavioral responses that include laughter, crying, or both. An affected individual exhibits episodes of laughter and / or crying without an apparent motivating stimulus or in response to stimuli that would not have elicited such an emotional response before the onset of their underlying neurologic disorder. Symptoms of PLC can be severe, and episodes can be persistent and unremitting. PLC is a clinical condition that occurs in patients with various neurological disorders. The majority of evidence relates to stroke patients. The review summarizes the available data about pharmacological and behavioral treatment efficiency for this condition. Furthermore the management of these patients adds a rich avenue to the future understanding of emotional behavior.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0130.003

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 head0.026
GPT teacher head0.264
Teacher spread0.238 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)→Same topicStroke Rehabilitation and Recovery→French-language works237,207→