Reliability of a Digital-Photographic-Goniometric Method for Coronal-Plane Lower Limb Measurements
Bibliographic record
Abstract
CONTEXT: Structural and coronal-plane-alignment characteristics of the lower limb are frequently cited as factors contributing to knee pathologies. OBJECTIVE: The purpose of this study was to determine the accuracy and reliability characteristics of a digital-photographic-goniometric method (DPGM) of measurement for 2-dimensional (2D) coronal-plane lower limb measurements of the quadriceps (Q) angle, tibiofemoral (TF) angle, and femur length in human participants adopting a self-selected- or Romberg-stance position. DESIGN: Reliability study. SETTING: University motion-analysis laboratory. PARTICIPANTS: A convenience sample of 20 healthy young adult men and women. MAIN OUTCOME MEASURES: Intraclass correlation coefficients (ICCs), 95% confidence intervals, and standard error of the measurements. RESULTS: Intratester- and intertester-reliability coefficients for the Q angle (ICCs .458-.845 and .257-.737) were consistently lower than those for the TF angle (ICCs .627-.904 and .700-.839) or femur length (ICCs .867-.958 and .866-.944). Q angles were also significantly larger (13.4%) in the Romberg- vs self-selected-stance position (P < .001) and larger (20.2%) in the left limb than the right limb. CONCLUSIONS: The DPGM has the potential to produce accurate and reliable measurements of selected 2D lower limb measures. However, the reliability characteristics depend on the ability of the testers to correctly and repeatably landmark the anatomical sites used to define the measurements of interest and might be influenced by other factors such as the stance position adopted, the complexity of the variable (ie, number of anatomical landmarks and segments), and the size of the captured image. Further investigation of these latter factors is warranted.
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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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".