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Record W4362555395 · doi:10.1080/24725579.2023.2188319

Mapping the process of ICU care delivery to improve treatment decisions in acute respiratory failure

2023· article· en· W4362555395 on OpenAlexaff
Jacqueline M. Kruser, Elizabeth M. Viglianti, Ruben Mylvaganam, Kristyn A. Krolikowski, Rebeca Khorzad, Michael E. Detsky, Douglas A. Wiegmann, Richard G. Wunderink, Jane L. Holl

Bibliographic record

VenueIISE Transactions on Healthcare Systems Engineering · 2023
Typearticle
Languageen
FieldHealth Professions
TopicFamily and Patient Care in Intensive Care Units
Canadian institutionsSinai Health SystemUniversity of Toronto
FundersNational Heart, Lung, and Blood InstituteNational Institutes of Health
KeywordsIntensive care medicineAcute respiratory failureRespiratory failureProcess (computing)MedicineMedical emergencyComputer scienceMechanical ventilationAnesthesia

Abstract

fetched live from OpenAlex

Evidence suggests system-level norms and care processes influence individual patients' medical decisions, including end-of-life decisions for patients with critical illnesses like acute respiratory failure. Yet, little is known about how these processes unfold over the course of a patient's critical illness in the intensive care unit (ICU). Our objective was to map current-state ICU care delivery processes for patients with acute respiratory failure and to identify opportunities to improve the process. We conducted a process mapping study at two academic medical centers, using focus groups and semi-structured interviews. The 70 participants represented 17 distinct roles in ICU care, including interprofessional medical ICU and palliative care clinicians, surrogate decision makers, and patient survivors. Participants refined and endorsed a process map of current-state care delivery for all patients admitted to the ICU with acute respiratory failure requiring mechanical ventilation. The process contains four critical periods for active deliberation about the use of life-sustaining treatments. However, active deliberation steps are inconsistently performed and frequently disrupted, leading to prolongation of life-sustaining treatment by default, without consideration of patients' individual goals and priorities. Interventions to standardize active deliberation in the ICU may improve treatment decisions for ICU patients with acute respiratory failure.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.676
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
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 head0.082
GPT teacher head0.360
Teacher spread0.278 · 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 designQualitative
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

Citations9
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueIISE Transactions on Healthcare Systems EngineeringSame topicFamily and Patient Care in Intensive Care UnitsFrench-language works237,207