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Record W2159043785 · doi:10.1186/cc2934

Bench-to-bedside review: Recruitment and recruiting maneuvers.

2004· review· en· W2159043785 on OpenAlexaff
Stephen E. Lapinsky, Sangeeta Mehta

Bibliographic record

VenueCritical Care · 2004
Typereview
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsUniversity of TorontoMount Sinai Hospital
Fundersnot available
KeywordsMedicineARDSPulmonary compliancePlateau pressureIntensive care medicinePositive end-expiratory pressureLungRespiratory physiologyTidal volumeMechanical ventilationHypoxemiaLung volumesAnesthesiaRespiratory systemInternal medicine

Abstract

fetched live from OpenAlex

In patients with acute respiratory distress syndrome (ARDS), the lung comprises areas of aeration and areas of alveolar collapse, the latter producing intrapulmonary shunt and hypoxemia. The currently suggested strategy of ventilation with low lung volumes can aggravate lung collapse and potentially produce lung injury through shear stress at the interface between aerated and collapsed lung, and as a result of repetitive opening and closing of alveoli. An 'open lung strategy' focused on alveolar patency has therefore been recommended. While positive end-expiratory pressure prevents alveolar collapse, recruitment maneuvers can be used to achieve alveolar recruitment. Various recruitment maneuvers exist, including sustained inflation to high pressures, intermittent sighs, and stepwise increases in positive end-expiratory pressure or peak inspiratory pressure. In animal studies, recruitment maneuvers clearly reverse the derecruitment associated with low tidal volume ventilation, improve gas exchange, and reduce lung injury. Data regarding the use of recruitment maneuvers in patients with ARDS show mixed results, with increased efficacy in those with short duration of ARDS, good compliance of the chest wall, and in extrapulmonary ARDS. In this review we discuss the pathophysiologic basis for the use of recruitment maneuvers and recent evidence, as well as the practical application of the technique.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.808
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.240
GPT teacher head0.480
Teacher spread0.240 · 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 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

Citations104
Published2004
Admission routes1
Has abstractyes

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