Using digital photography to document rectus femoris flexibility: A reliability study of the modified Thomas test
Bibliographic record
Abstract
BACKGROUND: Research indicates that rectus femoris muscle flexibility assessment techniques suffer from multiple sources of measurement error. OBJECTIVE: To examine whether scoring of rectus femoris muscle flexibility from digital photographs of clinical examination using the modified Thomas test would be highly reliable. METHODS: Twenty-eight individuals were digitally photographed while having their rectus femoris muscle flexibility evaluated using the modified Thomas test. Therapists were then asked to view these digital photographs and score participant's flexibility using modified Thomas test scoring criteria. A retest session was completed approximately 1-week later. RESULTS: Kappa values for positive/negative scoring (intra-rater experienced X¯ = 0.86, in-experienced X¯ = 0.98; interrater experienced X¯ = 0.95, in-experienced X¯ = 0.99) and ICC values for goniometer scoring (intra-rater experienced X¯ = 0.98, in-experienced X¯ = 0.98; interrater experienced X¯ = 0.97, in-experienced X¯ = 0.98) indicated very high levels of reliability. Measurement error values (SEM = 1.0°, ME = 1.53°, and CV = 3%) and Bland and Altman plots (with 95% limits of agreement) further illustrated the very small degree of scoring variance. CONCLUSIONS: Results indicate that goniometer and positive/negative scoring of rectus femoris muscle flexibility from digital photographs of clinical examination using the modified Thomas test were highly reliable. This finding suggests that using digital photography as a means to document patient function during clinical examination may serve as a method to help standardize physical assessment, minimize error measurement, and assist the clinician/researcher in establishing whether an observed change between testing sessions is clinically significant.
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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 | 0.023 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".