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Record W2169901874 · doi:10.1164/rccm.201406-1117pp

The Role of Stepdown Beds in Hospital Care

2014· review· en· W2169901874 on OpenAlexaff
Meghan Prin, Hannah Wunsch

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2014
Typereview
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
FundersNational Institute on Aging
KeywordsMedicineIntensive care medicine

Abstract

fetched live from OpenAlex

Stepdown beds provide an intermediate level of care for patients with requirements somewhere between that of the general ward and the intensive care unit. Models of care include incorporation of stepdown beds into intensive care units, stand-alone units, or incorporation of beds into standard wards. Stepdown beds may be used to provide a higher level of care for patients deteriorating on a ward ("step-up"), a lower level of care for patients transitioning out of intensive care ("stepdown") or a lateral transfer of care from a recovery room for postoperative patients. These units are one possible strategy to improve critical care cost-effectiveness and patient flow without compromising quality, but these potential benefits remain primarily theoretical as few patient-level studies provide concrete evidence. This narrative review provides a general overview of the theory of stepdown beds in the care of hospitalized patients and a summary of what is known about their impact on patient flow and outcomes and highlights areas for future research.

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)0.004
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.005
Science and technology studies0.0010.003
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.032
GPT teacher head0.339
Teacher spread0.307 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations163
Published2014
Admission routes1
Has abstractyes

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