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Record W2973272059 · doi:10.1097/aco.0000000000000795

Anesthesia for thoracic ambulatory surgery

2019· review· en· W2973272059 on OpenAlexaff
Julien Raft, Philippe Richebé

Bibliographic record

VenueCurrent Opinion in Anaesthesiology · 2019
Typereview
Languageen
FieldMedicine
TopicNausea and vomiting management
Canadian institutionsUniversité de MontréalHôpital Maisonneuve-Rosemont
Fundersnot available
KeywordsMedicineAmbulatoryCardiothoracic surgeryAnesthesiaThoracoscopyNerve blockSurgery

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Ambulatory surgery plays a major role in cost-effective patient care without compromising patient safety and satisfaction. This concept improves the patient support and decreases the length of stay sometimes until ambulatory surgery. The aim of this review is to examine the current state of the art of anesthesia for thoracic ambulatory surgery. RECENT FINDINGS: Guidelines for enhanced recovery after thoracic surgery (ERATS) have recently been published. They can be safely implemented without increasing hospital readmission or mortality. Video-assisted thoracoscopy may be the best approach within a fast-track program. Anesthetic management should focus on combination of regional analgesia and general anesthesia techniques. General anesthesia should be performed with short acting agent and prevention of residual paralysis. Thoracic epidural analgesia is the gold standard technique for pain control after major thoracic surgery but not compatible with a quick hospital discharge. Thoracic paravertebral block, Serratus plane block, intercostal nerve block, and more recently erector spinae plane block have all been used with success for analgesia in thoracic surgery. CONCLUSION: ERATS program may lead to improved outcomes including decreased length of stay, but it is currently too early to show the impact on thoracic ambulatory surgery that concerned selected patients for lung resection.

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)0.000
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 score0.946
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.330
GPT teacher head0.483
Teacher spread0.153 · 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

Citations18
Published2019
Admission routes1
Has abstractyes

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