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Record W2147843063 · doi:10.1186/cc1045

Utility of routine chest radiographs in a medical–surgical intensive care unit: a quality assurance survey

2001· article· en· W2147843063 on OpenAlexaff
Natalie Chahine-Malus, Thomas E. Stewart, Stephen E. Lapinsky, Ted Marras, David Dancey, Richard Leung, Sangeeta Mehta

Bibliographic record

VenueCritical Care · 2001
Typearticle
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsMount Sinai Hospital
Fundersnot available
KeywordsMedicineChest radiographEmergency medicineRadiographyIntensive care unitQuality assuranceProspective cohort studyIntensive care medicineSurgery

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine the utility of routine chest radiographs (CXRs) in clinical decision-making in the intensive care unit (ICU). DESIGN: A prospective evaluation of CXRs performed in the ICU for a period of 6 months. A questionnaire was completed for each CXR performed, addressing the indication for the radiograph, whether it changed the patient's management, and how it did so. SETTING: A 14-bed medical-surgical ICU in a university-affiliated, tertiary care hospital. PATIENTS: A total of 645 CXRs were analyzed in 97 medical patients and 205 CXRs were analyzed in 101 surgical patients. RESULTS: Of the 645 CXRs performed in the medical patients, 127 (19.7%) led to one or more management changes. In the 66 surgical patients with an ICU stay <48 hours, 15.4% of routine CXRs changed management. In 35 surgical patients with an ICU stay > or = 48 hours, 26% of the 100 routine films changed management. In both the medical and surgical patients, the majority of changes were related to an adjustment of a medical device. CONCLUSIONS: Routine CXRs have some value in guiding management decisions in the ICU. Daily CXRs may not, however, be necessary for all patients.

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)0.001
metaresearch head score (Gemma)0.045
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.045
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.133
GPT teacher head0.463
Teacher spread0.330 · 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

Citations50
Published2001
Admission routes1
Has abstractyes

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