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Record W4415788712 · doi:10.1186/s12910-025-01299-x

Association between critical care occupancy and code status decisions during resource scarcity: a retrospective cohort study

2025· article· en· W4415788712 on OpenAlexaff
Stijn Bex, Lorna Guinness, Christophe Gaudet-Blavignac, Jeremy Martin, Jérôme Stirnemann, Thomas Agoritsas, Anne Rossel, Antonio Leidi, Olivier Grosgurin, Jean‐Luc Reny, Christophe A. Fehlmann, Samia Hurst-Majno, Christophe Marti

Bibliographic record

VenueBMC Medical Ethics · 2025
Typearticle
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsMcMaster University
FundersUniversité de Genève
KeywordsRetrospective cohort studyTriagePhilosophy of medicineProxy (statistics)OccupancyAssociation (psychology)Health careTransparency (behavior)

Abstract

fetched live from OpenAlex

BACKGROUND: Code status determination typically relies on the expected benefits and harms of treatment intensification and patient values and preferences. Resource availability may also influence code status decisions. During the COVID-19 pandemic, the demand for critical care often exceeded the available resources. This study investigated the association between critical care occupancy and code status decisions during the COVID-19 pandemic. METHODS: We conducted a retrospective cohort study of adult patients hospitalized at Geneva University Hospital for acute COVID-19-related illness during two successive pandemic waves, in spring and autumn 2020. Multivariable logistic regression was used to analyze the association between critical care occupancy at admission and code status attribution while accounting for clinical and demographic characteristics, including age, sex, ROX index (pulse oximetry/fraction of inspired oxygen/respiratory rate), comorbidities, malignancy, nationality, insurance, and socioeconomic status. RESULTS: A total of 2,122 patients were included in the analysis. Higher critical care occupancy was associated with an increased likelihood of being assigned an intensive care unit (ICU)-ineligible code status. The odds ratios (ORs) were 1.61 (95% CI 1.11-2.32), 1.59 (1.11-2.28) and 1.71 (1.06-2.76) for critical care occupancy levels of 100-119%, 120-139% and ≥ 140%, respectively, compared with the prepandemic baseline capacity. Other factors significantly associated with the assignment of an ICU-ineligible code status included age 70-79 years (OR 8.56; 95% CI 4.12-17.77), 80-89 years (OR 32.78; 95% CI 16.16-66.50) and ≥90 years (OR 49.04; 95% CI 23.05-104.31) and a higher comorbidity index (OR 1.22; 95% CI 1.07-1.39). Conversely, complementary hospitalization insurance was associated with lower odds of being assigned an ICU-ineligible code status (OR 0.52; 95% CI 0.29-0.92). CONCLUSIONS: Our study revealed a positive association between critical care occupancy and ICU-ineligible code status, suggesting the presence of implicit triaging during periods of high resource strain. This raises several ethical concerns, including the use of non-consensual triage criteria, lack of transparency and the risk of moral distress for healthcare professionals.

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)0.002
metaresearch head score (Gemma)0.125
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.123
Threshold uncertainty score0.883

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.125
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.117
GPT teacher head0.449
Teacher spread0.332 · 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 designObservational
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
Published2025
Admission routes1
Has abstractyes

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