Acute Pain: Effective Management Requires Comprehensive Assessment
Bibliographic record
Abstract
Pain is among the most common reasons that patients seek medical care, and inadequate assessment may result in suboptimal management. Acute pain in response to trauma or surgery can be complex, variable, and dynamic, but its assessment is often simplistic and brief. One-dimensional rating scale measures of pain severity facilitate rapid evaluation and often form the basis of treatment algorithms. However, additional features of pain should inform the selection of a treatment regimen, and can include pain qualities, duration, impact on functional capabilities, and underlying cause. Patient age, sex, psychosocial features, and comorbid conditions are also important features to consider. Use of a multidimensional tool is recommended for assessing many of these features if time permits. Additionally, clinicians often fail to recognize or consider the potentially detrimental long-term effects of acute pain. As the United States continues to experience a prescription drug crisis, a "universal precautions" approach including abuse risk assessment and abuse deterrence strategies should be implemented for patients receiving opioids. Increased efforts and research are necessary to enhance the utility of available acute pain assessment tools. Developing more comprehensive tools for patient assessment is the first step in achieving the ultimate goal of effective acute pain management. The objectives of this review are to summarize issues regarding the complexity of acute pain and to provide suggestions for its evaluation.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".