Bibliographic record
Abstract
A pain management guideline was developed at the Royal Columbian Hospital, New Westminster, British Columbia, to prevent pain after cardiac surgery. The guideline was based on a wellness model and was predicted on the World Health Organization's analgesic ladder. Patients are given nonopioids around the clock and throughout the postoperative stay and are given an opioid to prevent procedural pain and treat breakthrough pain. In an evaluation of the guideline, records from 133 cardiac surgery patients were retrospectively reviewed. The type and dose of analgesics administered for the first 6 days after surgery, the effectiveness of the pain management plan, the occurrence of adverse effects, time to extubation, and postoperative lengths of stay were determined. Ninety-five percent of patients had effective pain relief. Almost all patients received acetaminophen around the clock. A total of 89% received indomethacin. All patients received opioids intermittently. Doses of opioids were converted to morphine oral equivalents, which peaked on day 1 after surgery (38 equivalents) and decreased sharply by day 2 (< 10 equivalents). Median postoperative length of stay was 5 days for patients who had bypass surgery and 6 days for patients who had valve surgery. This proactive, low-tech, low-risk, well-tolerated pain management approach is cost-effective, simple, and feasible to use. The findings support use of this approach in managing pain after cardiac surgery.
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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 | 0.035 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".