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Pain Related Functional Limitations of Persons Injured in Car Accidents

2021· article· en· W3205946108 on OpenAlexaff
Stephan C. Mann, Varadaraj R. Velamoor, Larry C. Litman, Zack Z. Cernovsky

Bibliographic record

VenueEuropean Journal of Clinical Medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsWestern University
Fundersnot available
KeywordsRivermead post-concussion symptoms questionnaireBrief Pain InventoryAngerPsychologyAnxietyPhysical therapyMoodClinical psychologyPsychiatryMedicineChronic painTraumatic brain injury

Abstract

fetched live from OpenAlex

Background: In medical psychology, the Brief Pain Inventory (BPI) allows for a separate assessment of pain intensity (scales of worst, least, and average pain) and of daily functional limitations due to pain (impairments of mood, ability to walk, work, interpersonal relations, sleep, and enjoyment of life). The present study evaluates the convergent validity of BPI’s measure of such functional limitations by calculating its correlations to other relevant clinical measures of psychological impairments caused by motor vehicle accidents (MVAs). Method: De-identified archival data were available on 50 persons injured in MVAs (age 20 to 86 years, mean=42.1 years, SD=16.4; 23 males, 27 females). Their MVA occurred 11 to 280 weeks prior to psychological testing with the BPI (average time lapse 73.3 weeks, SD=53.8). All patients were still experiencing active post-MVA symptoms requiring medical attention and therapy. With respect to convergent validity, we examined Pearson correlations of the BPI to the Insomnia Severity Index (ISI), Rivermead Post-Concussion Symptoms Scale, Subjective Neuropsychological Symptoms Scale (SNPSS), and to measures of depression, anger, and anxiety (Items 10 to 12 of the Whiplash Disability Questionnaire). Results: Functional interference of pain with daily activities (sum of BPI Items 9B to 9G) correlated significantly at p<0.05, 2-tailed with Rivermead post-concussion scores (r=0.39), post-MVA subjective neuropsychological symptoms (r=0.45), insomnia scores (r=0.41), and ratings of depression (r=0.52), anger (r=0.46), and anxiety (r=0.44). When the sum of BPI ratings of worst, least, and average pain was added to the functional interference/limitations score, then this sum of 9 BPI items correlated significantly at p<0.05, 2-tailed with Rivermead post-concussion scores (r=0.36), post-MVA subjective neuropsychological symptoms (r=0.46), insomnia scores (r=0.37), and ratings of depression (r=0.53), anger (r=0.50), and anxiety (r=0.40). Discussion and Conclusion: The results lend support to convergent validity of the BPI when applied to persons injured in vehicular accidents.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.368
GPT teacher head0.450
Teacher spread0.082 · 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 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

Citations1
Published2021
Admission routes1
Has abstractyes

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