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Record W4412479558 · doi:10.1001/jamasurg.2025.2226

Clinical Practice Guideline Recommendations on Mental Health in Trauma

2025· article· en· W4412479558 on OpenAlexaff
Mélanie Berube, Alexandra Lapierre, William J. Panenka, Meaghan O’Donnell, Nori Bradley, Lynne Moore, Laurence Bourque, Mickael Thébaud, Patrick Archambault, Léonie Archambault, Alexis F. Turgeon, Juanita A. Haagsma, Naisan Garraway, Matthew Menear, Michel Perreault, Marc‐Aurèle Gagnon, Helen‐Maria Vasiliadis, Christine Genest, Hélène Provencher, Henry T. Stelfox

Bibliographic record

VenueJAMA Surgery · 2025
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsUniversity of AlbertaUniversité de SherbrookeMcGill UniversityInstitut Universitaire en Santé Mentale de QuébecDouglas Mental Health University InstituteCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalUniversity of Alberta HospitalUniversité de MontréalUniversity of SaskatchewanRoyal Columbian HospitalAlberta Hospital EdmontonUniversity of British ColumbiaUniversité LavalVancouver General HospitalCentres Intégré Universitaires de Santé et de Services SociauxCentre hospitalier universitaire de Québec
Fundersnot available
KeywordsMedicineCINAHLGuidelinePsycINFOMEDLINEMental healthReferralGrading (engineering)Family medicineCochrane LibraryEvidence-based medicineNursingPsychological interventionPsychiatryAlternative medicinePathology

Abstract

fetched live from OpenAlex

Importance: Many survivors of traumatic injuries are affected by mental disorders, which has recently led to the publication of clinical practice guidelines (CPGs). However, there is no comprehensive synthesis of guideline recommendations to inform clinicians on those that should be prioritized for implementation and thus promote adherence to them. Objective: To identify guideline recommendations for the prevention and management of mental disorders in patients with traumatic injuries, appraise their quality, and synthesize the quality of evidence and the strength of included recommendations. Evidence Review: MEDLINE, Embase, CINAHL, PsycINFO, Cochrane Central, Web of Science, and 61 websites of professional associations and guideline repositories were searched between January 2008 and September 2024. We included CPGs pertinent to the acute and early recovery phases (<3 months) of adult patients (≥18 years) with traumatic injuries with at least 1 recommendation on mental health. Pairs of reviewers independently extracted data and assessed guideline quality using the Appraisal of Guidelines Research and Evaluation (AGREE) II tool. The quality of evidence on recommendations was synthesized using a matrix based on the categories of the Grading of Recommendations, Assessment, Development and Evaluation (GRADE). Mental health recommendations had to target prevention, screening, evaluation, intervention, referral for follow-up or specialized services, and a patient- and family-centered care approach. Findings: Forty-three CPGs were included, 25 of which (58%) were high quality. Rigor of development, applicability, lack of involvement from all interested parties, and editorial independence were the most common methodological weaknesses. High-quality CPGs included 200 recommendations; of these, 50 (25%) were supported by moderate- to high-quality evidence and 30 (60%) targeted patients with traumatic brain injury. They covered mainly nonpharmacological and pharmacological interventions to treat acute stress disorder, substance use disorders, posttraumatic stress disorder, depression, or aggression. Fewer recommendations related to prevention, screening, evaluation, and referral were identified as having high empirical support. Conclusions and Relevance: Fifty recommendations were identified that may be considered for implementation in clinical settings in patients with traumatic brain injury and other trauma populations. Our review underlines important areas for future research, including training for clinicians, a patient- and family-centered care approach, and health care equity.

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)0.011
metaresearch head score (Gemma)0.046
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.449
Threshold uncertainty score0.962

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.046
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.264
GPT teacher head0.575
Teacher spread0.311 · 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.

Study designNot applicable
Domainnot available
GenreCommentary

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
Published2025
Admission routes1
Has abstractyes

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