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Record W2808233011 · doi:10.1638/2017-0167.1

ANESTHESIA OF AQUARIUM-HOUSED WALRUS (<i>ODOBENUS ROSMARUS</i>): A CASE SERIES

2018· article· en· W2808233011 on OpenAlexaff
Johanna Kaartinen, Stéphane Lair, Michael T. Walsh, Patrick Burns, Marion Desmarchelier, Jessica Pang, Daniel Pang

Bibliographic record

VenueJournal of Zoo and Wildlife Medicine · 2018
Typearticle
Languageen
FieldMedicine
TopicThermal Regulation in Medicine
Canadian institutionsCegep de Saint Hyacinthe
Fundersnot available
KeywordsMedicineAnesthesia

Abstract

fetched live from OpenAlex

Walruses are a challenging species to anesthetize as a result of their large mass, limited access for drug delivery, unique physiology, and small number of reports describing anesthetic procedures. Three aquarium-housed walruses ( Odobenus rosmarus) ranging in age from 3 to 11 yr old (344-1,000 kg) were anesthetized for dental or ophthalmic surgical procedures, with one animal anesthetized twice and one anesthetized three times. Preanesthetic medication was with intramuscular midazolam (0.1-0.2 mg/kg) and meperidine (2-3 mg/kg). A catheter was placed in the extradural intravertebral vein, and anesthesia was induced with propofol to effect. Orotracheal intubation was performed and anesthesia maintained with isoflurane in oxygen using a circle breathing system connected to a ventilator. Intermittent positive pressure ventilation was used in all procedures. For the ophthalmic surgery, the neuromuscular blocking agent, cisatracurium, was given intravenously to provide a central eye and optimal surgical conditions. The neuromuscular block was antagonized with edrophonium. Total anesthesia times ranged from 1.5 to 6 hr. Midazolam and meperidine were antagonized with flumazenil and naltrexone, respectively, in five of six cases. Nonsteroidal anti-inflammatory agents were provided for analgesia. Recoveries were calm and uneventful. The described anesthetic protocols and case management were successful under the conditions encountered.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.399
Threshold uncertainty score0.602

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.016
GPT teacher head0.281
Teacher spread0.265 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
Domainnot available
GenreEmpirical

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

Citations4
Published2018
Admission routes1
Has abstractyes

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