What Should be the Optimal Cut Points for Mild, Moderate, and Severe Pain?
Bibliographic record
Abstract
PURPOSE: Grouping patients' rating of pain intensity from 0 to 10 into categories of mild, moderate, and severe pain is useful for informing treatment decisions, interpreting study outcomes, as well as aiding policy or clinical practice guidelines development. In 1995, Serlin and colleagues developed a technique to establish the cut points for mild, moderate, and severe pain by grading pain intensity with functional interference. Since then, a number of studies attempted to confirm these findings in similar or different populations but had different results. Such inconsistencies in the literature prompt for more research to establish the definition of mild, moderate and severe pain. Thus, the purpose of the current study was to identify optimal cut points (CP) of the three pain severity categories for worst, average, and current pain. PATIENTS AND METHODS: The study population (n = 199) was patients with symptomatic bone metastases referred to a palliative radiotherapy clinic. Using the Brief Pain Inventory (BPI), patients reported their worst, average, and current pain intensity, as well as the degree of functional interference due to pain. All possible combinations for the CPs, between 2 and 8, were created and related to the set of 7 interference items from the BPI using the multivariate analysis of variance (MANOVA). The criteria used to determine the optimal set of cut points for mild, moderate and severe pain was a MANOVA among pain severity categories that yielded the largest F ratio for the between-category effect on the 7 interference items as indicated by Pillai's trace, Wilk's lambda, and Hotelling's trace F statistics. RESULTS: Results confirmed a non-linear relationship between cancer pain severity and functional interference. The optimal CP for worst and average pain was CP4, 6 (mild = 1-4, moderate = 5-6, and severe = 7-10), confirming Serlin and colleagues's findings. CONCLUSION: These findings are pivotal in further understanding the meaning of pain intensity levels and the assessment of pain in patients with metastatic cancer. However, further research in alternative methods of defining the optimal CP and clinically important change should be considered.
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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 | 0.035 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".