MétaCan
Menu
Back to cohort
Record W1970548425 · doi:10.1097/mcc.0b013e3283220df2

Mechanical ventilation: epidemiological insights into current practices

2009· review· en· W1970548425 on OpenAlexaff
Ewan C. Goligher, Niall D. Ferguson

Bibliographic record

VenueCurrent Opinion in Critical Care · 2009
Typereview
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsCARE CanadaUniversity Health NetworkUniversity of TorontoToronto Western Hospital
Fundersnot available
KeywordsMedicineMechanical ventilationPopulationIntensive care medicineRandomized controlled trialObservational studyEpidemiologyPneumoniaComorbidityVentilation (architecture)Incidence (geometry)Emergency medicineInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: To describe the trends in the results of epidemiological studies of mechanical ventilation. RECENT FINDINGS: Changes in population demographics have increased the incidence of mechanical ventilation. Higher age and comorbidity rates portend poorer outcomes of mechanical ventilation. The most common indication for initiation of mechanical ventilation is acute respiratory failure, including postoperative respiratory failure, pneumonia, sepsis, and acute respiratory distress syndrome. Patients with sepsis and acute respiratory distress syndrome have a much higher mortality risk than the rest of this population. Changes over time in the selection of modes of ventilation, tidal volumes, positive end-expiratory pressure levels, weaning strategies, and tracheostomy timing appear to accord with data from randomized controlled trials in the literature. However, despite these changes, observational studies have not detected a statistically significant change in adjusted mortality over time. SUMMARY: The burden of critical illness will likely continue to increase in the future. Evidence from randomized trials appears to have affected the management of mechanical ventilation, but adherence to evidence-based practices may not be ideal.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.953
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.479
GPT teacher head0.569
Teacher spread0.090 · 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

Citations40
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueCurrent Opinion in Critical CareSame topicRespiratory Support and MechanismsFrench-language works237,207