Adverse outcomes associated with opioid prescription for acute low back pain: a systematic review protocol
Bibliographic record
Abstract
Acute low back pain (ALBP) is the top cause of global disability, demonstrating a significant impact on individuals and society and demanding the need for appropriate management. There is a trend towards an increasing number of opioid prescriptions for ALBP despite the lack of investigation for its various short- and long-term outcomes. The objective of this review is to examine adverse outcomes associated with opioid use for ALBP. Using a search strategy, the search will be conducted using the following electronic databases: PubMed/MEDLINE, EMBASE, PsycINFO, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Web of Science, Cochrane Library, the National Institutes for Health Clinical Trials Registry and the World Health Organization International Clinical Trials Registry Platform (WHO ICTRP). We will include randomized clinical trials and observational studies investigating the impact of opioid use in ALBP in the adult population. All phases of screening, data extraction and assessment of methodological quality will be performed by two independent reviewers. We will perform quality and risk of bias assessment for the included articles and compare high and low risk of bias with a sensitivity analysis. We will conduct random- and fixed-effects meta-analyses with heterogeneity calculated using the I 2 statistic and evaluate publication bias. There are current guidelines published to alert clinicians in prescribing opioids for ALBP due to its likelihood of misuse, yet there is little change in prescribing patterns. To date, there is an absence of systematic information about the outcomes of prescription opioid in patients with ALBP. We will address this gap by providing evidence that will be useful for clinical practice. PROSPERO CRD42016033090
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.059 | 0.052 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.022 | 0.020 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.062 | 0.007 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".