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Record W2808504455 · doi:10.1186/s12888-018-1769-9

Prevalence of acute stress disorder among road traffic accident survivors: a meta-analysis

2018· review· en· W2808504455 on OpenAlex
Wenjie Dai, Aizhong Liu, Atipatsa Chiwanda Kaminga, Jing Deng, Zhiwei Lai, Jianzhou Yang, Shi Wu Wen

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBMC Psychiatry · 2018
Typereview
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersCanadian Institutes of Health ResearchNatural Science Foundation of Hainan ProvinceSpecialized Research Fund for the Doctoral Program of Higher Education of ChinaCentral South University
KeywordsMeta-analysisMedicinePsycINFOConfidence intervalAcute Stress DisorderTraumatic stressSubgroup analysisPsychiatryMEDLINEMental healthClinical psychologyPosttraumatic stressInternal medicine

Abstract

fetched live from OpenAlex

Road traffic accident (RTA), an unexpected traumatic event, may not only lead to death and serious physical injuries, but also could put survivors at an increased risk for a wide range of psychiatric disorders, particularly acute stress disorder (ASD). Early assessment of trauma-related psychological responses is important because acute trauma responses in the early post-traumatic period are among the robust predictors of long-term mental health problems. However, estimates of the prevalence of ASD among RTA survivors varied considerably across studies. Therefore, this meta-analysis aimed to identify the pooled prevalence of ASD among RTA survivors. A systematic literature search in the databases of PubMed, PsycINFO, PsycARTICLES, Embase and Web of Science was performed from their inception dates to December 2017. Subject headings were used to identify relevant articles, and the search strategy was adjusted across databases. Heterogeneity across studies was evaluated by Cochran’s χ2 test and quantified by the I2 statistic. Subgroup analyses were performed to identify the pooled prevalence in relation to the country of study, instrument used to identify ASD, age, gender and traumatic brain injury. When significant heterogeneity was observed, the influence of some potential moderators was explored using meta-regression analyses. Thirteen eligible studies conducted in 8 countries were included. A total of 2989 RTA survivors were assessed, of which 287 were identified with ASD. The overall heterogeneity was high across studies (I2=96.8%, P < 0.001), and the pooled prevalence of ASD among RTA survivors was 15.81% (95% confidence interval: 8.27–25.14%). Subgroup analyses indicated that the prevalence of ASD among RTA survivors differed significantly with regard to the country of study, instrument used to identify ASD, age and gender (P < 0.05). Meta-regression analyses showed that mean age of participants and quality assessment score were significant moderators for heterogeneity (P < 0.05). Nearly one-sixth of RTA survivors suffer from ASD, indicating the need for regular assessment of early trauma responses among RTA survivors, as well as the importance of implementing early psychological interventions.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.157
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.010
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0030.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0170.001

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 head0.158
GPT teacher head0.440
Teacher spread0.282 · 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