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Record W2948678423 · doi:10.31636/pmjua.v4i1.6

Clinical case of postoperative anesthesia of a patient by using subanesthetic dose of ketamine in severe abdominal pathology

2019· article· en· W2948678423 on OpenAlexaff
Bohdan Zaletskyi, V A Korobko, Dmytro Dmytrіiev

Bibliographic record

VenuePain medicine · 2019
Typearticle
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMedicineKetamineAnesthesiaAnestheticGabapentinAcetaminophenDoseAnalgesicOpioidPharmacologyInternal medicine

Abstract

fetched live from OpenAlex

Pain is an inevitable consequence of surgical interventions in children, resulting in great stress and discomfort not only for patients but also for their parents. The intensity of the pain depends not only on the level of injury after the operation, but also on the localization and the nature of the procedure. Management of pain in children is best done through a multimodal approach: opioids, auxiliary drugs such as nonsteroidal anti-inflammatory drugs (NSAIDs) and acetaminophen, anti-neuroleptics such as gabapentin, and regional anesthetic methods. Postoperative anesthesia in abdominal surgery at present is a topical problem in anesthetic practice. In this clinical case, we would like to demonstrate the experience of applying post-operative anesthesia using subnormal dosages of ketamine. The patient was given anesthesia with prolonged infusion of a ketamine solution in a submorbid dose of 0.2 mg/kg/h IV. An assessment of the quality of anesthesia by assessing the level of stress markers, such as blood glucose, cortisol levels, and the assessment of the pain level on the NIPS scale was performed. Conclusion: The use of a ketamine solution in a dose of 0.2 mg/kg/h has a positive effect on treating postoperative pain in patients after severe abdominal surgical interventions. Applying a ketamine solution in a dose of 0.2 mg/kg/h reduces tolerance of the patient to opioid analgesics and the development of hyperalgesia and allodynia.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.375
Threshold uncertainty score0.608

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.019
GPT teacher head0.308
Teacher spread0.289 · 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 designObservational
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

Citations0
Published2019
Admission routes1
Has abstractyes

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