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Record W4393086211 · doi:10.1186/s13054-024-04873-8

The long-term intercorrelation between post-burn pain, anxiety, and depression: a post hoc analysis of the “RE-ENERGIZE” double-blind, randomized, multicenter placebo-controlled trial

2024· article· en· W4393086211 on OpenAlexafffund
Adriana C. Panayi, Daren K. Heyland, Christian Stoppe, Marc G. Jeschke, Oliver Didzun, Dany Y. Matar, Christian Tapking, Alen Palackic, Björn Bliesener, Leila Harhaus, Samuel Knoedler, Valentin Haug, Amir K. Bigdeli, Ulrich Kneser, Dennis P. Orgill, Gabriel Hundeshagen

Bibliographic record

VenueCritical Care · 2024
Typearticle
Languageen
FieldMedicine
TopicBurn Injury Management and Outcomes
Canadian institutionsMcMaster UniversityHamilton Health SciencesClinical Evaluation Research UnitQueen's University
FundersCanadian Institutes of Health ResearchU.S. Department of Defense
KeywordsMedicineAnxietyPsychosocialPopulationPost-hoc analysisDepression (economics)Randomized controlled trialPhysical therapyPlaceboPropensity score matchingPatient Health QuestionnaireCohortInternal medicinePsychiatryDepressive symptoms

Abstract

fetched live from OpenAlex

BACKGROUND: Despite the growing prevalence of burn survivors, a gap persists in our understanding of the correlation between acute burn trauma and the long-term impact on psychosocial health. This study set out to investigate the prevalence of long-term pain and symptoms of anxiety and depression in survivors of extensive burns, comparing this to the general population, and identify injury and demographic-related factors predisposing individuals to psychosocial compromise. METHODS: RE-ENERGIZE was an international, double-blinded, randomized-controlled trial that enrolled 1200 patients with partial- or full-thickness burns that required surgical treatment. For the post hoc analysis, we excluded participants who did not complete the Short Form Health Survey (SF-36) questionnaire. Normative data were taken from the 2021 National Health Interview Survey dataset. Propensity score matching was performed using the nearest-neighbor 1-to-1 method, and the two cohorts were compared in terms of chronic pain, and symptoms of anxiety and depression. A multivariable analysis was performed on the burns cohort to identify factors predicting post-discharge pain and symptoms of anxiety and depression. RESULTS: A total of 600 burn patients and 26,666 general population adults were included in this study. Following propensity score matching, both groups comprised 478 participants each, who were predominately male, white, overweight and between 20 and 60 years old. Compared to the general population, burn patients were significantly more likely to report the presence of moderate and a lot of pain (p = 0.002). Symptoms of anxiety were significantly higher in the burn population in two of four levels (most of the time; some of the time; p < 0.0001 for both). Responders in the burn population were significantly less likely to report the absence of depressive symptoms (p < 0.0001). Burn patients were also significantly more likely to report that their mental health affects their social life. TBSA, history of depression, and female sex were identified as independently associated factors for pain, anxiety, and depressive symptoms. The presence of chronic pain and anxiety symptoms independently predicted for symptoms of depression. CONCLUSIONS: Analyzing the largest multicenter cohort of patients with extensive burns, we find that burn injury is associated with chronic pain, and symptoms of anxiety and depression. In addition, TBSA-burned and history of depression directly correlate with the prevalence of chronic pain, and symptoms of anxiety and depression. Finally, pain, and symptoms of anxiety and depression are interrelated and may have interactive effects on the process of recovery following burn injury. Burn patients would, therefore, benefit from a multidisciplinary team approach with early mobilization of pain and mental health experts, in order to promptly prevent the development of psychosocial challenges and their consequences.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.436

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.022
GPT teacher head0.334
Teacher spread0.312 · 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 designRandomized trial
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

Citations25
Published2024
Admission routes2
Has abstractyes

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