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Record W4302763297 · doi:10.1097/corr.0000000000002329

What Is the Prevalence of Intimate Partner Violence and Traumatic Brain Injury in Fracture Clinic Patients?

2022· article· en· W4302763297 on OpenAlexaffabout
Lyn S. Turkstra, Krista Salanki, Emily MacIntyre, Noel N. Kim, Jonathan Jin, Sheila Sprague, Taryn Scott, Mohit Bhandari

Bibliographic record

VenueClinical Orthopaedics and Related Research · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineTraumatic brain injuryDomestic violenceTrauma centerInjury preventionPoison controlOccupational safety and healthEmergency departmentOutpatient clinicSuicide preventionPhysical therapyPediatricsPsychiatryMedical emergencyRetrospective cohort studySurgeryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Individuals in violent intimate relationships are at a high risk of sustaining both orthopaedic fractures and traumatic brain injury (TBI), and the fracture clinic may be the first place that concurrent intimate partner violence (IPV) and TBI are recognized. Both IPV and TBI can affect all aspects of fracture management, but prevalence of TBI and comorbid TBI and IPV is unknown. QUESTIONS/PURPOSES: (1) What are the previous-year and lifetime prevalence of IPV and TBI in women presenting to an outpatient orthopaedic fracture clinic? (2) What are the conditional probabilities of TBI in the presence of IPV and the reverse, to explore whether screening for one condition could effectively identify patients with the other? (3) Do patients with TBI, IPV, or both have worse neurobehavioral symptoms than patients without TBI and IPV? METHODS: The study was completed in the fracture clinic at a community Level 1 trauma center in Southern Ontario from July 2018 to March 2019 and included patients seen by three orthopaedic surgeons. Inclusion criteria were self-identification as a woman, age 18 years or older, and the ability to complete forms in English without assistance from the person who brought them to the clinic (for participant safety and privacy). We invited 263 women to participate: 22 were ineligible (for example, they were patients of a surgeon who was not on the study protocol), 87 declined before hearing the topic of the study, and data from eight were excluded because the data were incomplete or lost. Complete data were obtained from 146 participants. Participants' mean age was 52 ± 16 years, and the most common diagnosis was upper or lower limb fracture. Prevalence of IPV was calculated as the number of women who answered "sometimes" or "often" to direct questions from the Woman Abuse Screening Tool, which asks about physical, emotional, and sexual abuse in the past year or person's lifetime. The prevalence of TBI was calculated as the number of women who reported at least one head or neck injury that resulted in feeling dazed or confused or in loss of consciousness lasting 30 minutes or less on the Ohio State University Traumatic Brain Injury Identification Method, a standardized procedure for eliciting lifetime history of TBI through a 3- to 5-minute structured interview. Conditional probabilities were calculated using a Bayesian analysis. Neurobehavioral symptoms were characterized using the Neurobehavioral Symptom Inventory, a standard self-report measure of everyday emotional, somatic, and cognitive complaints after TBI, with total scores compared across groups using a one-way ANOVA. RESULTS: Previous-year prevalence of physical IPV was 7% (10 of 146), and lifetime prevalence was 28% (41 of 146). Previous-year prevalence of TBI was 8% (12 of 146), and lifetime prevalence was 49% (72 of 146). The probability of TBI in the presence of IPV was 0.77, and probability of IPV in the presence of TBI was 0.36. Thus, screening for IPV identified proportionately more patients with TBI than screening for TBI, but the reverse was not true. Neurobehavioral Symptom Inventory scores were higher (more symptoms) in patients with TBI only (23 ± 16) than those with fractures only (12 ± 11, mean difference 11 [95% CI 8 to 18]; p < 0.001), in those with IPV only (17 ± 11) versus fractures only (mean difference 5 [95% CI -1 to -11]; p < 0.05), and in those with both TBI and IPV (25 ± 14) than with fractures only (mean difference 13 [95% CI 8 to 18]; p < 0.001) or those with IPV alone (17 ± 11, mean difference 8 [95% CI -1 to 16]; p < 0.05). CONCLUSION: Using a brief screening interview, we identified a high self-reported prevalence of TBI and IPV alone, consistent with previous studies, and a novel finding of high comorbidity of IPV and TBI. Given that the fracture clinic may be the first healthcare contact for women with IPV and TBI, especially mild TBI associated with IPV, we recommend educating frontline staff on how to identify IPV and TBI as well as implementing brief screening and referral and universal design modifications that support effective, efficient, and accurate communication patients with TBI-related cognitive and communication challenges. LEVEL OF EVIDENCE: Level II, prognostic study.

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)0.008
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 score0.120
Threshold uncertainty score0.239

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.082
GPT teacher head0.462
Teacher spread0.379 · 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".

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Citations10
Published2022
Admission routes2
Has abstractyes

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