Pain Assessment Using Labeled Face Scale
Bibliographic record
Abstract
The Face Scale consists of a set of 6 faces that vary in the level of overt distress expressed.Subjects choose 1 face from the series of pain-expressing faces that best represents the current status of their pain.However,it is an ordinal scale that does not ensure equivalence for the differences between faces.In this study,we conducted 2 experiments with the objective of evaluating the differences between faces and creating a pain scale that is easy to use in pain assessment.In experiment 1,the pain intensity evoked by 6 faces was measured using Scheffe’s method of paired comparisons,and each face was converted to a position within a 100-mm linear pain scale depending on the pain intensity.By doing this,we created a pain scale in the form of a Labeled Face Scale graduated at 0.00,10.02,18.61,40.31,62.22 and 100.00.In experiment 2,patients’pain was measured and compared using the Labeled Face Scale and a magnitude estimation method.Correlation analysis showed that there was a positive correlation between them (R2=0.8127)and Bland-Altman analysis showed that the majority of plots (94.4%) fell within the coefficient of repeatability (±2 SD).These results lead us to conclude that the Labeled Face Scale is a useful assessment tool for pain management.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".