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Record W4285215912 · doi:10.4103/sja.sja_236_22

Perioperative Pain Management in Bariatric Anesthesia

2022· review· en· W4285215912 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSaudi Journal of Anaesthesia · 2022
Typereview
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicinePerioperativeAnesthesiaPain managementSurgery

Abstract

fetched live from OpenAlex

Weight loss (bariatric) surgery is the most commonly performed elective surgical procedure in patients with morbid obesity. In this review, we provide an evidence-based update on perioperative pain management in bariatric anesthesia. We mention some newer preoperative aspects-medical optimization, physical preparation, patient education, and psychosocial factors-that can all improve pain management. In the intraoperative period, with bariatric surgery being almost universally performed laparoscopically, we emphasize the use of non-opioid adjuvant infusions (ketamine, lidocaine, and dexmedetomidine) and suggest some novel regional anesthesia techniques to reduce pain, opioid requirements, and side effects. We discuss some postoperative strategies that additionally focus on patient safety and identify patients at risk of persistent pain and opioid use after bariatric surgery. This review suggests that the use of a structured, step-wise, severity-based, opioid-sparing multimodal analgesic protocol within an enhanced recovery after surgery (ERAS) framework can improve postoperative pain management. Overall, by incorporating all these aspects throughout the perioperative journey ensures improved patient safety and outcomes from pain management in bariatric 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.

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)0.005
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 score0.979
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.035
GPT teacher head0.307
Teacher spread0.272 · 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