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Intrathecal Meperidine and Shivering in Obstetric Anesthesia

2004· article· en· W4250964939 on OpenAlexaffabout
Jean-Denis Roy, Michel Girard, Pierre Drolet

Bibliographic record

VenueAnesthesia & Analgesia · 2004
Typearticle
Languageen
FieldMedicine
TopicThermal Regulation in Medicine
Canadian institutionsHôpital Maisonneuve-Rosemont
Fundersnot available
KeywordsShiveringNauseaMedicineVomitingAnesthesiaMetoclopramideIncidence (geometry)IntrathecalPethidineAnestheticAnalgesicSurgery

Abstract

fetched live from OpenAlex

In Response: We thank Yu et al. for their interest in our article. In our study, no difference in the incidence of nausea was noted, as reflected by the similar doses of metoclopramide used in each group. It is always difficult to compare results from different studies; populations differ, surgical technique may differ, and the extent of abdominal exploration may differ in each study. However, in each of our studies (1,2), nausea and vomiting were secondary outcomes, thus larger groups of patients will be necessary for a definitive answer on this issue. In the study by Booth et al. (3), doses of 15–25 mg of meperidine were used and patients were in labor, which increases the incidence of nausea and vomiting. Shivering was graded with a scale described by Crossley and Mahajan (4). Most of our patient were graded 2 (“muscular activity in only one muscular group”) and above. This is enough to make patients uncomfortable, and therefore reduction of this shivering is an advantage. Although we understand the reticence of Yu et al. in giving prophylactic intrathecal meperidine, we believe that under proper conditions, this provides the parturient with excellent anesthetic and analgesic conditions with few side effects, while providing the added comfort of preventing shivering. Jean-Denis Roy, MD Michel Girard, MD, MHPE, FRCP(C) Pierre Drolet, MD, FRCP(C) Département d’Anesthésiologie Hôpital Maisonneuve-Rosemont Montréal, Canada [email protected]

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)0.000
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 score0.192
Threshold uncertainty score0.961

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.251
Teacher spread0.239 · 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

Citations1
Published2004
Admission routes2
Has abstractyes

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