Frequency of Opioid Prescribing for Acute Low Back Pain in a Rural Emergency Department
Bibliographic record
Abstract
National guidelines created by the Agency for Healthcare Research and Quality (AHRQ), the American College of Emergency Physicians (ACEP), and the American College of Physicians (ACP) support the use of nonsteroidal anti-inflammatory drugs (NSAIDs) over opioids when treating acute low back pain (; ; ). Opioids not only have many more side effects than NSAIDs but also carry the risk of opioid abuse and overdose (). The purpose of this study was to determine whether emergency department (ED) providers, including physicians, nurse practitioners, and physician assistants, are following evidence-based low back pain management guidelines by assessing the measurement of opioid versus NSAID prescribing. A retrospective chart review including data from January through June 2017 was conducted at a rural ED. Subject inclusion criteria were as follows: older than 18 years, had experienced new-onset low back pain within the last 1 month, and had been given an ICD-10 (International Classification of Diseases, Tenth Revision) code of M54.5. Data regarding the type of provider seen, the treatment the provider prescribed, and demographics were collected. Inclusion criteria were met by 162 subjects. While 52.5% of subjects were prescribed an NSAID at discharge, 53.7% were prescribed an opioid at discharge. Subjects whose injury was work related were less likely to receive an opioid prescription (p = 0.027, 95% CL). Subjects whose pain started within 3 days were more likely to receive an opioid prescription than those whose pain had started more than 3 days before being seen (p = 0.018, 95% CL). Despite the current evidence-based guidelines by the AHRQ, ACEP, and ACP against opioid prescribing for acute low back pain, more subjects received an opioid prescription at discharge than a prescription for an NSAID. This retrospective chart review determined the need for increased ED provider education regarding treatment modalities for acute low back pain.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
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 teacher head, 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".