Multidimensional versus unidimensional pain scales for the assessment of analgesic requirement in the emergency department: a systematic review
Bibliographic record
Abstract
Pain is a multidimensional experience, potentially rendering unidimensional pain scales inappropriate for assessment. Prior research highlighted their inadequacy as reliable indicators of analgesic requirement. This systematic review aimed to compare multidimensional with unidimensional pain scales in assessing analgesic requirements in the emergency department (ED). Embase, Medline, CINAHL, and PubMed Central were searched to identify ED studies utilizing both unidimensional and multidimensional pain scales. Primary outcome was desire for analgesia. Secondary outcomes were amount of administered analgesia and patient satisfaction. Two independent reviewers screened, assessed quality, and extracted data of eligible studies. We assessed risk of bias with the ROBINS-I tool and provide a descriptive summary. Out of 845 publications, none met primary outcome criteria. Three studies analyzed secondary outcomes. One study compared the multidimensional Defense and Veterans Pain Rating Scale (DVPRS) to the unidimensional Numerical Rating Scale (NRS) for opioid administration. DVPRS identified more patients with moderate instead of severe pain compared to the NRS. Therefore, the DVPRS might lead to a potential reduction in opioid administration for individuals who do not require it. Two studies assessing patient satisfaction favored the short forms (SF) of the Brief Pain Inventory (BPI) and McGill Pain Questionnaire (MPQ) over the Visual Analogue Scale (VAS) and the NRS. Limited heterogenous literature suggests that in the ED, a multidimensional pain scale (DVPRS), may better discriminate moderate and severe pain compared to a unidimensional pain scale (NRS). This potentially impacts analgesia, particularly when analgesic interventions rely on pain scores. Patients might prefer multidimensional pain scales (BPI-SF, MPQ-SF) over NRS or VAS for assessing their pain experience.
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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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".