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Record W2155560701 · doi:10.1080/09540120500521244

Chronic pain and head injury following motor vehicle collisions: a double whammy or different sides of a coin

2006· article· en· W2155560701 on OpenAlexaff
Tony Iezzi, Melanie P. Duckworth, Victoria Mercer, Lieu N. Vuong

Bibliographic record

VenuePsychology Health & Medicine · 2006
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsLondon Health Sciences Centre
Fundersnot available
KeywordsHead (geology)MedicinePhysical medicine and rehabilitationGeology

Abstract

fetched live from OpenAlex

Chronic pain and head injury are common and burdensome sequelae of motor vehicle collisions. The aim of this study was to compare differences in physical injury and functional impairment, psychological distress and pain coping in head injured and non-head injured chronic pain persons subsequent to motor vehicle collisions. Two groups of 54 participants matched in terms of age, gender, and years of formal education underwent a psychological-legal assessment. As part of the assessment, participants completed the Multidimensional Pain Inventory, Sickness Impact Profile, Minnesota Multiphasic Personality Inventory-2, and Coping Strategies Questionnaires. Select scales from questionnaires were combined and underwent multivariate analyses of covariance to test the effects of pain sites at the time of psychological-legal assessment (low, high) and head injury status (head injured and non-head injured chronic pain). Overall, some differences between the two groups were noted but the results did not strongly support the hypothesis that head injured chronic pain participants have a greater physical or psychological burden than non-head injured chronic pain participants. The results suggest the import of assessing and managing pain sites and pain severity in persons injured in motor vehicle collisions.

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)0.002
metaresearch head score (Gemma)0.000
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 score0.475
Threshold uncertainty score0.544

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.028
GPT teacher head0.398
Teacher spread0.369 · 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

Citations13
Published2006
Admission routes1
Has abstractyes

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