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Record W1971725751 · doi:10.4103/0253-7176.106012

Pain Catastrophizing: An Updated Review

2012· article· en· W1971725751 on OpenAlexaff
Lawrence Leung

Bibliographic record

VenueIndian Journal of Psychological Medicine · 2012
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsQueen's University
Fundersnot available
KeywordsLearned helplessnessRuminationCoping (psychology)Pain catastrophizingFeelingPsychologyNeuroimagingPopulationCognitionClinical psychologyChronic painMedicinePsychiatrySocial psychology

Abstract

fetched live from OpenAlex

Pain catastrophizing has been described for more than half a century which adversely affects the pain coping behavior and overall prognosis in susceptible individuals when challenged by painful conditions. It is a distinct phenomenon which is characterized by feelings of helplessness, active rumination and excessive magnification of cognitions and feelings toward the painful situation. Susceptible subjects may have certain demographic or psychological predisposition. Various models of pain catastrophizing have been proposed which include attention-bias, schema-activation, communal-coping and appraisal models. Nevertheless, consensus is still lacking as to the true nature and mechanisms for pain catastrophizing. Recent advances in population genomics and noninvasive neuroimaging have helped elucidate the known determinants and neurophysiological correlates behind this potentially disabling 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)0.001
metaresearch head score (Gemma)0.004
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 score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0060.007
Science and technology studies0.0000.001
Scholarly communication0.0020.004
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.002

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 head0.045
GPT teacher head0.392
Teacher spread0.347 · 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

Citations290
Published2012
Admission routes1
Has abstractyes

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