Pregabalin Has Opioid-Sparing Effects Following Augmentation Mammaplasty
Bibliographic record
Abstract
BACKGROUND: There has been a recent, growing concern regarding narcotic use in surgical patients. This issue, coupled with an ongoing desire to lessen postoperative discomfort, has prompted the search for alternative analgesic regimens. OBJECTIVE: The purpose of this study was to determine whether the addition of pregabalin, an anticonvulsant indicated for the management of neuropathic pain, to an analgesic regimen reduced narcotic use and reported pain following augmentation mammaplasty. METHODS: Eighty patients underwent submuscular augmentation mammaplasty with smooth shell saline mammary prostheses in an outpatient surgical facility. The patients were randomized into two groups. Group A (n = 40) used 5-mg hydrocodone tablets as needed to manage postoperative pain. Group B (n = 40) used pregabalin, 75 mg, twice daily in addition to 5-mg hydrocodone tablets as needed for postoperative pain management. Narcotic use was recorded and pain assessed daily using the Rogers Pain Scale from 1 (mild) to 10 (severe). Patients were surveyed for nausea and quality of pain. RESULTS: Group A used 115 +/- 32 mg hydrocodone during the immediate 7 day postoperative period and reported an average pain scale score of 5.3. Likewise, group B used 33 +/- 27 mg hydrocodone as well as the prescribed pregabalin dosage and reported an average pain scale score of 3.4. Patients in group B reported less nausea. These differences were statistically significant (P < .05). Patient age, implant size, and postoperative complications were similar between the two groups. CONCLUSIONS: Perioperative pregabalin administration in patients undergoing augmentation mammaplasty reduced postoperative narcotic use by 70%. There was also significantly less reported pain and a 46% reduction in nausea in the pregabalin-treated group. Pregabalin has few side effects, no drug interactions, and should be considered safe in an analgesic regimen.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".