Low-dose intravenous ketamine for postcardiac surgery pain: Effect on opioid consumption and the incidence of chronic pain
Bibliographic record
Abstract
BACKGROUND: Recent meta-analyses have concluded that low-dose intravenous ketamine infusions (LDKIs) during the postoperative period may help to decrease acute and chronic postoperative pain after major surgery. AIMS: This study aims to evaluate the level of pain at least 3 months after surgery for patients treated with a postoperative LDKI versus patients who were not treated with a postoperative LDKI. METHODS: Administrative and Ethics Board approval were obtained for this study. We performed a retrospective chart review for all patients receiving LDKI, and equal number of age-, sex-, and surgery-matched patients who did not receive LDKI. Low-dose ketamine was prepared using 100 mg of ketamine in 100 ml of normal saline and run between 50 and 200 mcg/kg/h. RESULTS: We reviewed 115 patients with LDKI and 115 without LDKI. The average age was 63.1 years, 73% of the patients were men and sex was evenly distributed between LDKI and non-LDKI. The average duration of the ketamine infusions was 26.8 h with the average dose being 169.9 mg. At an average of 9 months after surgery, 42% of the ketamine group and 38% of the nonketamine group stated that they had had pain on discharge. Of these patients, 30% of the ketamine group and 26% of the nonketamine group still had pain at the time of the phone call. Women in both groups had more acute and chronic pain than men. CONCLUSION: These results show that LDKI does not promote a decrease in long-term postoperative pain.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".