Opioid Consumption After Upper Extremity Surgery: A Systematic Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
There is currently an overprescription of opioids, which may result in abuse and diversion of narcotics. The aim of this systematic review was to investigate opioid prescription practices and consumption by patients after upper extremity surgery. This review was registered a priori on Open Science Framework (osf.io/6u5ny) and adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A search strategy was performed using MEDLINE, Embase, PubMed, and Cochrane Central Register of Controlled Trials databases (from their inception to October 17, 2021). Prospective studies investigating opioid consumption of patients aged 18 years or older undergoing upper extremity surgeries were included. The Risk of Bias in Nonrandomized Studies of Interventions and Risk of Bias 2.0 tools were used for quality assessment. In total, 21 articles met the inclusion criteria, including 7 randomized controlled trials and 14 prospective cohort studies. This represented 4195 patients who underwent upper extremity surgery. Most patients took less than half of the prescribed opioids. The percentage of opioids consumed ranged from 11% to 77%. There was moderate to severe risk of bias among the included studies. This review demonstrated that there is routinely excessive opioid prescription relative to consumption after upper limb surgery. Additional randomized trials are warranted, particularly with standardized reporting of opioid consumption and assessment of patient-reported outcomes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| 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) | 0.001 | 0.006 |
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 it