The Reliability of Measuring Pain Distribution and Location Using Body Pain Diagrams in Patients With Acute Whiplash-Associated Disorders
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to measure the interexaminer reliability of scoring pain distribution using paper and electronic body pain diagrams in patients with acute whiplash-associated disorder and to assess the intermethod reliability of measuring pain distribution and location using paper and electronic diagrams. METHODS: We conducted an interexaminer reliability study on 80 participants recruited from a randomized controlled trial on the conservative management of acute grade I/II whiplash-associated disorder. Participants were assessed for inclusion/exclusion criteria by an experienced clinician. As part of the baseline assessment, participants independently completed paper and electronic pain diagrams. Diagrams were scored independently by 2 examiners using the body region method. Interexaminer and intermethod reliability was computed using intraclass correlation coefficients (ICCs) for pain distribution and κ coefficient for pain location. We used Bland-Altman plots to compute limits of agreement. RESULTS: The interexaminer reliability was ICC = 0.925 for paper and ICC = 0.997 for the electronic body pain diagram. The intermethod reliability for measuring pain distribution ranged from ICC = 0.63 to ICC = 0.93. For pain location, the intermethod reliability varied from κ = 0.23 (posterior neck) to κ = 0.90 (right side of the face). CONCLUSIONS: We found good to excellent interexaminer reliability for scoring 2 versions of the body pain diagram. Pain distribution and pain location were reliably and consistently measured on body pain diagrams using paper and electronic methods; therefore, clinicians and researchers may choose either medium when using body pain diagrams.
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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 | 0.017 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| 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".