A smartphone version of the Faces Pain Scale‐Revised and the Color Analog Scale for postoperative pain assessment in children
Bibliographic record
Abstract
BACKGROUND: Effective pain assessment is essential during postoperative recovery. Extensive validation data are published supporting the Faces Pain Scale-Revised (FPS-R) and the Color Analog Scale (CAS) in children. Panda is a smartphone-based application containing electronic versions of these scales. OBJECTIVES: To evaluate agreement between Panda and original paper/plastic versions of the FPS-R and CAS and to determine children's preference for either Panda or original versions of these scales. METHODS: ASA I-III children, 4-18 years, undergoing surgery were assessed using both Panda and original versions of either the FPS-R or CAS. Pain assessments were conducted within 10 min of waking from anesthesia and 30 min later. RESULTS: Sixty-two participants, median (range) age 7.5 (4-12) years, participated in the FPS-R trial; Panda scores correlated strongly with the original scores at both time points (Pearson's r > 0.93) with limits of agreement within clinical significance (80% CI). Sixty-six participants, age 13 (5-18) years, participated in the CAS trial. Panda scores correlated strongly with the original scores at both time points (Pearson's r > 0.87); mean pain scores were higher (up to +0.47 out of 10) with Panda than with the original tool, representing a small systematic bias, but limits of agreement were within clinical significance. Most participants who expressed a preference preferred Panda over the original tool (81% of FPS-R, 76% of CAS participants). CONCLUSION: The Panda smartphone application can be used in lieu of the original FPS-R and CAS for assessment of pain in children. Children's preference for Panda may translate to improved cooperation with self-report of pain.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".