Intrathecal Meperidine and Shivering in Obstetric Anesthesia
Bibliographic record
Abstract
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]
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".