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PP222 Topic: AS21–Post-PICU: Patient and Family Outcomes/Chronic Critical Illness/Post Intensive Care Syndrome in Pediatrics (PICS-p)/Post-discharge Care Delivery Models/Other: SYSTEMATIC REVIEW AND META-ANALYSIS OF PREVALENCE AND POPULATION-LEVEL FACTORS CONTRIBUTING TO POSTTRAUMATIC STRESS DISORDER IN PEDIATRIC INTENSIVE CARE SURVIVORS

2024· article· en· W4404041871 on OpenAlexaff
Rebecca E. Hay, Katie O’Hearn, David J. Zorko, L.A. Lee, Sarah Mooney, Christopher Finn McQuaid, Lisa Albrecht, David Henshall, V. Campes Dannenburg, Verônica Indicatti Fiamenghi, Céline Thibault, WK Lee, Michelle Shi Min Ko, Michele Cree, Jean St. Louis, Julia A. Heneghan, Karen Ka Yan Leung, Amanda Wood, Eliana López, Mohamad‐Hani Temsah, Mohammed Almazyad, Jennifer Retallack, Maya Reddy, Nedaa Aldairi, RA Palomino, K. Choong, Geneviève Du Pont‐Thibodeau, Laurence Ducharme‐Crevier, Dayre McNally, Gonzalo Garcia Guerra

Bibliographic record

VenuePediatric Critical Care Medicine · 2024
Typearticle
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsUniversity of TorontoUniversity of British ColumbiaUniversity of AlbertaUniversity of CalgaryCentre Hospitalier Universitaire Sainte-JustineMcMaster UniversityUniversity of Ottawa
Fundersnot available
KeywordsMedicineMeta-analysisCritical illnessIntensive careIntensive care medicinePediatricsCritically illInternal medicine

Abstract

fetched live from OpenAlex

Aims & Objectives: Pediatric intensive care unit (PICU) admission is a life-threatening event placing survivors at risk for posttraumatic stress disorder (PTSD). This systematic review sought to describe the prevalence of PTSD in PICU survivors and analyze potential risk-factors driving variation in prevalence estimates. Methods: MEDLINE, Embase, CINAHL, and CENTRAL databases were searched from 2000 to August 2022, no language restrictions. Stage one screening of abstract and full text sought to identify studies evaluating PICU survivors post-discharge. Stage two screening identified specific studies reporting on incidence or prevalence of PTSD and/or risk factors. Given the large number of citations, we utilized a validated hybrid model of machine learning and crowdsourcing, recruiting a global community at the WFPICCS 2022 meeting. Random effects meta-analysis calculated pooled PTSD prevalence and evaluated subgroup differences. Results: From the original 15950 citations, 22 met criteria with a median cohort size 59 (IQR 49-76). PTSD prevalence ranged from 3% to 37%, with 529 of the total 1898 survivors reported as having PTSD (I2= 72%). Study factors impacting PTSD variability included months assessed post-PICU (p<0.01) and instrument utilized (n=9, range 4-27%; p=0.03). Patient diagnosis mattered, with lower PTSD (8%) in post-operative cardiac patients (p<0.01). Interestingly, PICU length of stay did not impact PTSD, although heterogeneity was high. Conclusions: 1 in 4 PICU survivors (29%) developed PTSD six months post-PICU. We identified numerous population and study design factors that influence prevalence and heterogeneity. This review demonstrates the need for more standardization in post-PICU PTSD clinical application and research. Trial registration: PROSPERO, CRD42022348997 Keywords: PTSD, Systematic Review, Post PICU

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)0.005
metaresearch head score (Gemma)0.022
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 score0.023
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.009
Bibliometrics0.0080.013
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0230.002

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 head0.037
GPT teacher head0.330
Teacher spread0.292 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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