The Effect of Gabapentin on Delayed Discharge from the Postanesthesia Care Unit: A Retrospective Analysis
Bibliographic record
Abstract
BACKGROUND: Enhanced recovery after surgery programs has incorporated gabapentin as part of a multimodal analgesia protocol. The preemptive use of gabapentin was found to be beneficial due to its opioid-sparing effect. However, excessive sedation and delayed discharge from postanesthesia recovery units are of concern. The aim of this study was to investigate whether preoperative gabapentin increased the length of stay in the recovery unit. METHODS: This retrospective cross-sectional study was carried out over a period of 2 months in the postanesthesia care unit (PACU) of a tertiary care hospital in Canada. Two hundred and twenty-eight consecutive patients who underwent elective surgical procedures and who required a longer than 2-hour stay in the PACU were included. Prolonged stays caused by respiratory inadequacy, hemodynamic instability, nausea, vomiting, pain, and loss of consciousness were recorded. The data were collected from patients' charts and nursing flow sheets. RESULTS: All patients were grouped into those who received 300 mg gabapentin (n = 108), 600 mg gabapentin (n = 41), and no gabapentin (n = 139). No significant difference was observed between the groups in terms of opioid consumption, respiratory inadequacy, nausea, vomiting, and hemodynamic parameters. Gabapentin administration groups had significantly lower postoperative pain scores (P < 0.001). Decreased level of consciousness occurred significantly more often in a dose-dependent fashion in the gabapentin groups and led to a longer stay in the PACU (P < 0.001). CONCLUSION: In the setting of enhanced recovery after surgery, gabapentin did reduce pain scores, but at the cost of delayed discharge from the recovery room. Future studies are needed to evaluate the efficacy of gabapentin in this setting.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".