Reliability of the Craniocervical Posture Assessment: Visual and Angular Measurements Using Photographs and Radiographs
Bibliographic record
Abstract
OBJECTIVE: The purposes of this study were to determine the intrarater and interrater reliability of the craniocervical posture in a sagittal view using quantitative measurements on photographs and radiographs and to determine the agreement of the visual assessment of posture between raters. METHODS: One photograph and 1 radiograph of the sagittal craniocervical posture were simultaneously taken from 39 healthy female subjects. Three angles were measured on the photographs and 10 angles on the radiographs of 22 subjects using Alcimage software (Alcimage; Uberlândia, MG, Brazil). Two repeated measurements were performed by 2 raters. The measurements were compared within and between raters to test the intrarater and interrater reliability, respectively. Intraclass correlation coefficient and SEM were used. κ Agreement was calculated for the visual assessment of 39 subjects using photographs and radiographs between 2 raters. RESULTS: Good to excellent intrarater and interrater intraclass correlation coefficient values were found on both photographs and radiographs. Interrater SEM was large and clinically significant for cervical lordosis photogrammetry and for 1 angle measuring cervical lordosis on radiographs. Interrater κ agreement for the visual assessment using photographs was poor (κ = 0.37). CONCLUSION: The raters were reliable to measure angles in photographs and radiographs to quantify craniocervical posture with exception of 2 angles measuring lordosis of the cervical spine when compared between raters. The visual assessment of posture between raters was not reliable.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 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".