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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 OpenAlex
Michelle Y. Wong

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

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.

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 score0.959
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.134
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