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Record W2014838895 · doi:10.14740/jocmr1939w

Triage of Patients Consulted for ICU Admission During Times of ICU-Bed Shortage

2014· article· en· W2014838895 on OpenAlexvenueno aff
Orsini

Bibliographic record

VenueJournal of Clinical Medicine Research · 2014
Typearticle
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineTriageEconomic shortageEmergency medicineMedical emergencyIntensive care medicine

Abstract

fetched live from OpenAlex

BACKGROUND: The demand for specialized medical services such as critical care often exceeds availability, thus rationing of intensive care unit (ICU) beds commonly leads to difficult triage decisions. Many factors can play a role in the decision to admit a patient to the ICU, including severity of illness and the need for specific treatments limited to these units. Although triage decisions would be based solely on patient and institutional level factors, it is likely that intensivists make different decisions when there are fewer ICU beds available. The objective of this study is to evaluate the characteristics of patients referred for ICU admission during times of limited beds availability. METHODS: A single center, prospective, observational study was conducted among consecutive patients in whom an evaluation for ICU admission was requested during times of ICU overcrowding, which comprised the months of April and May 2014. RESULTS: A total of 95 patients were evaluated for possible ICU admission during the study period. Their mean APACHE-II score was 16.8 (median 16, range 3 - 36). Sixty-four patients (67.4%) were accepted to ICU, 18 patients (18.9%) were triaged to SDU, and 13 patients (13.7%) were admitted to hospital wards. ICU had no beds available 24 times (39.3%) during the study period, and in 39 opportunities (63.9%) only one bed was available. Twenty-four patients (25.3%) were evaluated when there were no available beds, and eight of those patients (33%) were admitted to ICU. A total of 17 patients (17.9%) died in the hospital, and 15 (23.4%) expired in ICU. CONCLUSION: ICU beds are a scarce resource for which demand periodically exceeds supply, raising concerns about mechanisms for resource allocation during times of limited beds availability. At our institution, triage decisions were not related to the number of available beds in ICU, age, or gender. A linear correlation was observed between severity of illness, expressed by APACHE-II scores, and the likelihood of being admitted to ICU. Alternative locations outside the ICU in which care for critically ill patients could be delivered should be considered during times of extreme ICU-bed shortage.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.439
GPT teacher head0.604
Teacher spread0.165 · 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 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

Citations45
Published2014
Admission routes1
Has abstractyes

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