Predictors of Post‐operative Pain and Opioid Consumption in Patients Undergoing Liver Surgery
Bibliographic record
Abstract
BACKGROUND: Post-operative pain management is a critical component of perioperative care. Patients at risk of poorly controlled post-operative pain may benefit from early measures to optimize pain management. We sought to identify risk factors for post-operative pain and opioid consumption in patients undergoing liver resection. METHODS: This is a multi-institutional prospective nested cohort study of patients undergoing open liver resection. Opioid consumption and pain scores were collected following surgery. To estimate the effects of patient factors on opioid consumption (oral morphine equivalents-OME) and on pain scores (NRS-11), we used generalized linear models and multivariable linear regression model, respectively. RESULTS: One hundred and fifty-three patients who underwent open liver resection between 2013 and 2016 were included in the study. The mean patient age was 62.2 years, and 43.3% were female. Younger patients were significantly more likely to use more opioids in the early post-operative period (16.7 OME/10 years, p < 0.001). Patient factors that were significantly associated with increased NRS-11 pain scores also included younger patient age (difference in pain score of 0.3/10 years with cough and 0.2/10 years at rest, p < 0.01 for both) as well as a history of analgesic use (difference in pain score of 0.9 with cough and 0.6 at rest, p < 0.01 and p = 0.07, respectively). CONCLUSION: Younger patients and those with a history of analgesic use are more likely to report higher post-operative pain and require higher doses of opioids. Early identification of these patients, and measures to better manage their pain, may contribute to optimal perioperative care.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".