Defining No Pain, Mild, Moderate, and Severe Pain Based on the Faces Pain Scale–Revised and Color Analog Scale in Children With Acute Pain
Bibliographic record
Abstract
OBJECTIVES: The aims of this study were to define the Faces Pain Scale-Revised (FPS-R) and Color Analog Scale (CAS) scores associated with no pain, mild pain, moderate pain, and severe pain in children with acute pain, and to identify differences based on age, sex, and ethnicity. METHODS: We conducted a prospective observational study in 2 pediatric emergency departments of children aged 4 to 17 years with painful and nonpainful conditions. We assessed their pain intensity using the FPS-R, CAS, and qualitative measures. Pain score cut points that best differentiated adjacent categories of pain were identified using a receiver operating characteristic-based method. Cut points were compared within subgroups based on age, sex, and ethnicity. RESULTS: We enrolled 620 patients, of whom 314 had painful conditions. The mean age was 9.2 years; 315 (50.8%) were in the younger age group (aged 4-7 years); 291 (46.8%) were female; and 341 (55%) were Hispanic. The scores best representing categories of pain for the FPS-R were as follows: no pain, 0 and 2; mild pain, 4; moderate pain, 6; and severe pain, 8 and 10. For the CAS, these were 0 to 1, 1.25 to 2.75, 3 to 5.75, and 6 to 10, respectively. Children with no pain frequently reported nonzero pain scores. There was considerable overlap of scores associated with mild and moderate pain. There were no clinically meaningful differences of scores representing each category of pain based on age, ethnicity, and race. CONCLUSIONS: We defined pain scores for the FPS-R and CAS associated with categories of pain intensity in children with acute pain that are generalizable across subgroups based on patient characteristics. There were minor but potentially important differences in pain scores used to delineate categories of pain intensity compared to prior convention.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".