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Record W3126777139 · doi:10.3390/psychiatryint2010004

Pain Sensitivity in Schizophrenia Spectrum Disorders: A Narrative Review of Recent Work

2021· review· en· W3126777139 on OpenAlexaff
Alexandre González-Rodríguez, Javier Labad, Mary V. Seeman

Bibliographic record

VenuePsychiatry International · 2021
Typereview
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsychosisContext (archaeology)PsychologyPerceptionNarrative reviewEndophenotypeSchizophrenia (object-oriented programming)Chronic painClinical psychologyStimulus modalityPain perceptionPsychiatryMedicinePsychotherapistPhysical therapyCognitionNeuroscience

Abstract

fetched live from OpenAlex

Many patients with schizophrenia seem relatively immune to physical pain while others complain of constant pain. This may result from disturbances or alterations of the sensory threshold for pain in populations with psychosis, a possibility for which there is some preliminary evidence. The inconsistency in pain perception may, in part, be explained by the treatments patients receive, but treatment-naïve patients also exhibit differences in response to pain. This suggests that decreased pain sensitivity may represent a specific psychosis endophenotype. Thus far, few experimental studies have investigated sensory thresholds, pain modalities, or other factors contributing to the perception or expression of physical pain in psychosis. A digital search for information on this topic was conducted in PubMed and Google Scholar. The result is a non-systematic, narrative review focusing on recent clinical and experimental findings of pain sensitivity in patients with psychosis. Importantly, physical and mental pain are closely connected constructs that may be difficult to differentiate. Our hope is that the review provides some clarity to the field in the specific context of schizophrenia.

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)0.005
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 score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0070.007
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.027
GPT teacher head0.355
Teacher spread0.328 · 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

Citations9
Published2021
Admission routes1
Has abstractyes

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