The Reliability of Body Pain Diagrams in the Quantitative Measurement of Pain Distribution and Location in Patients with Musculoskeletal Pain: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to perform a systematic review of test-retest, intraexaminer, and interexaminer reliability of measuring pain location and distribution using the body pain diagram. METHODS: We conducted a systematic review of the literature using a search conducted in Medline, CINAHL, and Nursing and Allied Health from inception to March 1, 2012. Articles were screened and selected by pairs of reviewers using predetermined inclusion criteria. Internal validity was assessed independently by 2 reviewers using a modified version of the QUADAS instrument. Articles with adequate internal validity were included in the best evidence synthesis. RESULTS: We reviewed 10 studies. Of those, 6 were included in the best evidence synthesis. We found varying levels of evidence that pain location and pain distribution can be measured reliably using the body pain diagram in patients with acute and chronic low back pain with or without radiculopathy. The test-retest reliability for measuring pain distribution ranged from intraclass correlation coefficient of 0.58 to 0.94. Similarly, the test-retest reliability for measuring pain location ranged from kappa (κ) of 0.13 to 0.85. The intraexaminer and interexaminer reliability for measuring pain distribution were intraclass correlation coefficient of 0.99 and 0.99, respectively. The intraexaminer and interexaminer reliability for measuring pain location ranged from κ of 0.77 to 0.88 and 0.61 to 1.00, respectively. CONCLUSIONS: We found important variations in the test-retest reliability of pain location and distribution across different test-retest scenarios and across body regions. The intraexaminer and interexaminer reliability for the measurement of pain distribution and pain location using the body pain diagram in patients with acute and chronic low back pain with or without radiculopathy are adequate.
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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 | 0.078 | 0.288 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.019 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".