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Attentional biases in pediatric chronic pain: an eye-tracking study assessing the nature of the bias and its relation to attentional control

2020· article· en· W3028318004 on OpenAlexafffund
Sabine Soltani, Dimitri Van Ryckeghem, Tine Vervoort, Lauren C. Heathcote, Keith Owen Yeates, Christopher R. Sears, Mélanie Noël

Bibliographic record

VenuePain · 2020
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsOntario Brain InstituteAlberta Children's HospitalUniversity of Calgary
FundersCanadian Institutes of Health ResearchAlberta Children's Hospital FoundationChildren's Hospital Foundation
KeywordsAttentional biasPsychologyChronic painGazeAttentional controlEye trackingFixation (population genetics)Eye movementDevelopmental psychologyCognitive psychologyCognitionAudiologyPsychiatryMedicineNeuroscience

Abstract

fetched live from OpenAlex

Attentional biases are posited to play a key role in the development and maintenance of chronic pain in adults and youth. However, research to date has yielded mixed findings, and few studies have examined attentional biases in pediatric samples. This study used eye-gaze tracking to examine attentional biases to pain-related stimuli in a clinical sample of youth with chronic pain and pain-free controls. The moderating role of attentional control was also examined. Youth with chronic pain (n = 102) and pain-free controls (n = 53) viewed images of children depicting varying levels of pain expressiveness paired with neutral faces while their eye gaze was recorded. Attentional control was assessed using both a questionnaire and a behavioural task. Both groups were more likely to first fixate on high pain faces but showed no such orienting bias for moderate or low pain faces. Youth with chronic pain fixated longer on all pain faces than neutral faces, whereas youth in the control group exhibited a total fixation bias only for high and moderate pain faces. Attentional control did not moderate attentional biases between or within groups. The results lend support to theoretical models positing the presence of attentional biases in youth with chronic pain. Further research is required to clarify the nature of attentional biases and their relationship to clinical outcomes.

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)0.008
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.752

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.046
GPT teacher head0.343
Teacher spread0.297 · 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 designObservational
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

Citations24
Published2020
Admission routes2
Has abstractyes

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