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Record W3191163180 · doi:10.1097/aln.0000000000003907

Opioid-free Anesthesia: Reply

2021· letter· en· W3191163180 on OpenAlexaff
Harsha Shanthanna, Karim S. Ladha, Henrik Kehlet, Girish P. Joshi

Bibliographic record

VenueAnesthesiology · 2021
Typeletter
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsSt. Joseph’s Healthcare HamiltonMcMaster UniversitySt. Joseph's Hospital
Fundersnot available
KeywordsMedicineOpioidPerioperativeAnesthesiaIntensive care medicinePolypharmacy

Abstract

fetched live from OpenAlex

We thank Forget et al.1 and Ingrande and Drummond2 for their interest in our review on perioperative opioid administration.3 Forget et al. contend that we did not distinguish between opioid-free anesthesia and opioid-free analgesia and ignored published studies and a meta-analysis.4 On the contrary, we explicitly distinguish between these two phases of care and even abbreviate them, so as to clarify our position throughout. Unfortunately, the definition of opioid-free anesthesia in literature seems to be loosely applied and consequentially misinterpreted. Whether opioid-free anesthesia means total abstinence or relative lack of intraoperative opioids is unclear. We discuss this as an important limitation of the review and meta-analysis by Frauenknecht et al.,4 in which included studies used opioids during the intraoperative period, thereby resulting in a potentially inappropriate conclusion.5 Furthermore, our statement that total avoidance of perioperative opioids has no influence on the long-term outcomes is based on evidence,6–8 contrary to the statement made by Forget et al.1 The most fundamental question is whether the goal of total opioid avoidance is really necessary and at what cost.Ingrande and Drummond2 draw attention to the fact that use of combination of medications (polypharmacy) is hazardous, which is indeed true. However, with regard to multimodal analgesia, we differ from their broad interpretation. The original definition of multimodal analgesia clarifies that the goal was to achieve sufficient analgesia due to synergistic effects between different group of analgesics, with accompanying reduction of side effects as one would be less dependent on a single analgesic modality.9 In our article, we clarify that the choice of what can be included as multimodal needs to be based on (1) intrinsic analgesic potency, (2) opioid-sparing potential, and (3) potential side effects. Bundling all modalities under nonopioid analgesics is inappropriate. We need to distinguish between adjuncts such as gabapentinoids, dexmedetomidine, lidocaine, ketamine, and magnesium versus known analgesics such as acetaminophen, nonsteroidal anti-inflammatory drugs, and cyclooxygenase-2–specific inhibitors or loco-regional techniques.10 In fact, acetaminophen and nonsteroidal anti-inflammatory drugs or cyclooxygenase-2–specific inhibitors should be administered to all surgical patients unless there are contraindications.10 Moreover, there are procedure-specific and patient-specific considerations, and a one-size-fits-all approach is not recommended. Because avoiding opioids, irrespective of the context, is seen to provide a compelling narrative in the background of the opioid crisis, analgesic practices seem to have resorted to multiple combinations of untested agents, overzealous application of drug combinations, or multiple interventions leading to toxicity and patient harms.11,12 We highlight the need for more balanced and responsible decision-making.Dr. Joshi has received honoraria from Baxter International Inc (Deerfield, Illinois) and Pacira Bioscience Inc (Parsippany, New Jersey). The other authors declare no competing interests.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.077
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.031
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.077
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0020.002
Science and technology studies0.0020.005
Scholarly communication0.0040.013
Open science0.0040.004
Research integrity0.0310.051
Insufficient payload (model declined to judge)0.0090.008

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 head0.022
GPT teacher head0.249
Teacher spread0.227 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

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Citations1
Published2021
Admission routes1
Has abstractyes

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