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Record W4407352460 · doi:10.1097/pcc.0000000000003696

Systematic Review and Meta-Analysis of Prevalence and Population-Level Factors Contributing to Posttraumatic Stress Disorder in Pediatric Intensive Care Survivors

2025· review· en· W4407352460 on OpenAlexaff
Rebecca E. Hay, Katie O’Hearn, David J. Zorko, Laurie A. Lee, Sarah Mooney, Lisa Albrecht, David Henshall, Vanessa Campes Dannenberg, Veronica Flamenghi, Céline Thibault, Wai Kit Lee, Michelle Shi Min Ko, Michele Cree, Julia St. Louis, Julia A. Heneghan, Karen Ka Yan Leung, Eliana López, Mohamad‐Hani Temsah, Mohammed Almazyad, Jennifer Retallack, Mounika Reddy, Nedaa Aldairi, Rubén Eduardo Lasso Palomino, Karen Choong, Geneviève Du Pont‐Thibodeau, Laurence Ducharme-Crevier, Anne Tsampalieros, Lamia Hayawi, James Dayre McNally, Gonzalo Garcia Guerra

Bibliographic record

VenuePediatric Critical Care Medicine · 2025
Typereview
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsBC Children's HospitalUniversity of TorontoUniversity of Alberta HospitalUniversity of AlbertaChildren's Hospital of Eastern OntarioUniversity of CalgaryCentre Hospitalier Universitaire Sainte-JustineMcMaster UniversityStollery Children's HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicineData extractionMEDLINEInterquartile rangeCINAHLPopulationCochrane LibraryMeta-analysisSystematic reviewPsychiatryPsychological interventionInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

OBJECTIVES: In survivors of illnesses or surgeries requiring PICU admission, there is a risk of posttraumatic stress disorder (PTSD). We aimed to estimate PTSD prevalence and potential contributing factors in survivors of PICU admission. DATA SOURCES: We performed a PROSPERO registered systematic review (CRD42022348997; Registered August 2022) using MEDLINE, Embase, CINAHL, and Cochrane Central Register of Controlled Trials (CENTRAL) databases, 2000 to 2022, with no language restrictions. STUDY SELECTION: Observational or interventional studies evaluating the incidence or prevalence of PTSD in patients' after PICU admission and/or contributing factors to PTSD. We used studies describing patients younger than 18 years old. Since there were a large number of citations, we used an integrated crowdsourcing and machine-learning model for citation screening. Each citation was reviewed independently and in duplicate by two reviewers at each stage of screening and abstraction. DATA EXTRACTION: Data items included study and participant demographics, details of case definition (PTSD screening), and risk factors. DATA SYNTHESIS: We followed the Preferred Reporting items for Systematic Reviews and Meta-analysis guidelines. Random-effects models were used to analyze PTSD prevalence and subgroup differences. In 24 citations meeting final review criteria, 19 had data for meta-analysis. There were 1898 PICU survivors with a median (interquartile range) cohort size of 59 (49-76). PTSD prevalence in the studies ranged from 3% to 37%; PTSD occurred in 529 of 1898 survivors ( I2 = 72%). Factors influencing PTSD variability included timing of assessment ( p < 0.01) with the highest prevalence (29%) at 6 months and the type of assessment instrument ( n = 10; range, 4-27%; p = 0.04). There was lower prevalence of PTSD (8%) in postoperative cardiac patients ( p < 0.01). Last, we failed to find an association between PICU length of stay and PTSD prevalence ( p = 0.62; I2 = 80%). CONCLUSIONS: PICU follow-up studies from 2000 to 2022 indicate that one-in-three of admissions surviving to 6 months have PTSD. However, there are population, study design factors and heterogeneity in PTSD assessment that indicate more standardization in this research is needed.

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)0.024
metaresearch head score (Gemma)0.077
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.024
Threshold uncertainty score0.129

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.077
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0240.037
Bibliometrics0.0120.013
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0030.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.067
GPT teacher head0.397
Teacher spread0.330 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations12
Published2025
Admission routes1
Has abstractyes

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