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Record W4383482463 · doi:10.1097/jtn.0000000000000735

Postintensive Care Syndrome: Feasibly Bridging Care at a Tertiary Trauma Center

2023· article· en· W4383482463 on OpenAlexaff
Timothy J. Stevens, Donna B. Lee

Bibliographic record

VenueJournal of Trauma Nursing · 2023
Typearticle
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsNortel (Canada)
Fundersnot available
KeywordsStaffingMedicineMultidisciplinary approachNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Advancements in critical care management have improved mortality rates of trauma patients; however, research has identified physical and psychological impairments that remain with patients for an extended time. Cognitive impairments, anxiety, stress, depression, and weakness in the postintensive care phase are an impetus for trauma centers to examine their ability to improve patient outcomes. OBJECTIVE: This article describes one center's efforts to intervene to address postintensive care syndrome in trauma patients. METHODS: This article describes implementing aspects of the Society of Critical Care Medicine's liberation bundle to address postintensive care syndrome in trauma patients. RESULTS: The implementation of the liberation bundle initiatives was successful and well received by trauma staff, patients, and families. It requires strong multidisciplinary commitment and adequate staffing. Continued focus and retraining are requirements in the face of staff turnover and shortages, which are real-world barriers. CONCLUSIONS: Implementation of the liberation bundle was feasible. Although the initiatives were positively received by trauma patients and their families, we identified a gap in the availability of long-term outpatient services for trauma patients after discharge from the hospital.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.596
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
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 head0.018
GPT teacher head0.301
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
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

Citations0
Published2023
Admission routes1
Has abstractyes

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