Opioid Consumption After Upper Extremity Surgery: A Systematic Review
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".