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Record W4226102338 · doi:10.2344/anpr-69-01-09

Reversal Agents in Sedation and Anesthesia Practice for Dentistry

2022· review· en· W4226102338 on OpenAlexaff
Michelle Y. Wong

Bibliographic record

VenueAnesthesia Progress · 2022
Typereview
Languageen
FieldMedicine
TopicAnesthesia and Sedative Agents
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineSugammadexSedationAnesthesiaNeostigmineContext (archaeology)FlumazenilMuscle relaxationNeuromuscular Blocking AgentsBenzodiazepinePropofolRocuroniumInternal medicine

Abstract

fetched live from OpenAlex

Reversal agents are defined as any drug used to counteract the pharmacologic effects of another drug. Several pharmacologic antagonists serve as essential drugs in the contemporary practices of sedation providers and anesthesiologists. Reversal or "antidote" drugs, such as flumazenil and naloxone, are often used in unintentional overdose situations involving significant benzodiazepine- and/or opioid-induced respiratory depression. Within the context of skeletal muscle relaxation, neostigmine and sugammadex are routinely used to reverse the effects of nondepolarizing neuromuscular blocking agents. In addition, the alpha-adrenergic antagonist phentolamine is used in dentistry as a local anesthetic reversal agent, decreasing its duration of action by inducing vasodilation. This review article discusses the pharmacology, uses, practical implications, adverse effects, and precautions needed for flumazenil, naloxone, neostigmine, sugammadex, and phentolamine within the context of sedation and anesthesia practice for dentistry.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.002

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 head0.134
GPT teacher head0.425
Teacher spread0.291 · 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
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

Citations3
Published2022
Admission routes1
Has abstractyes

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