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Record W2413813442 · doi:10.1097/aco.0000000000000267

Preoxygenation and intraoperative ventilation strategies in obese patients

2015· review· en· W2413813442 on OpenAlexaff
Ushma Jitendra Shah, Jean Wong, David T. Wong, Frances Chung

Bibliographic record

VenueCurrent Opinion in Anaesthesiology · 2015
Typereview
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsUniversity of TorontoToronto Western HospitalUniversity Health Network
Fundersnot available
KeywordsMedicineVentilation (architecture)Intensive care medicineMEDLINEAnesthesia

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Obesity along with its pathophysiological changes increases risk of intraoperative and perioperative respiratory complications. The aim of this review is to highlight recent updates in preoxygenation techniques and intraoperative ventilation strategies in obese patients to optimize gas exchange and pulmonary mechanics and reduce pulmonary complications. RECENT FINDINGS: There is no gold standard in preoxygenation or intraoperative ventilatory management protocol for obese patients. Preoxygenation in head up or sitting position has been shown to be superior to supine position. Apneic oxygenation and use of continuous positive airway pressure increases safe apnea duration. Recent evidence encourages the intraoperative use of low tidal volume to improve oxygenation and lung compliance without adverse effects. Contrary to nonobese patients, some studies have reported the beneficial effect of recruitment maneuvers and positive end-expiratory pressure in obese patients. No difference has been observed between volume controlled and pressure controlled ventilation. SUMMARY: The ideal ventilatory plan for obese patients is indeterminate. A multimodal preoxygenation and intraoperative ventilation plan is helpful in obese patients to reduce perioperative respiratory complications. More studies are needed to identify the role of low tidal volume, positive end-expiratory pressure, and recruitment maneuvers in obese patients undergoing general anesthesia.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.988
Threshold uncertainty score0.927

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.140
GPT teacher head0.432
Teacher spread0.292 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations47
Published2015
Admission routes1
Has abstractyes

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