Reliability of the Visual Assessment of Cervical and Lumbar Lordosis: How Good Are We?
Bibliographic record
Abstract
STUDY DESIGN: Blinded test-retest design. OBJECTIVE: To measure the intrarater and interrater reliability of the visual assessment of cervical and lumbar lordosis. SUMMARY OF BACKGROUND DATA: Cervical and lumbar lordoses are frequently evaluated using visual assessment, but little attempt has previously been made to measure the reliability of visual assessment. METHODS: Twenty-eight chiropractors, physical therapists, physiatrists, rheumatologists, and orthopedic surgeons were recruited to evaluate the posture of photographed subjects (with and without back pain). Each clinician rated the lordosis of the cervical and lumbar spines as normal, increased, or decreased. Kappa coefficients (kappa) were calculated to determine intrarater and interrater reliability. RESULTS: Twenty-eight clinicians evaluated photographs of 36 individuals (17 with back pain, 19 without). Mean intrarater reliability was kappa = 0.50 (95% confidence interval 0.02-0.98) and mean interrater reliability was kappa = 0.16 (95% confidence interval 0.00-0.48). No statistically significant difference existed among the five groups of clinicians or between the evaluation of the subjects with and without back pain. CONCLUSION: Intrarater reliability of the visual assessment of cervical and lumbar lordosis was statistically fair, whereas interrater reliability was poor.
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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 | 0.150 | 0.283 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".