Reliability and measurement error of the BioTonix Video Posture Evaluation System— Part I: Inanimate objects
Bibliographic record
Abstract
OBJECTIVE: To investigate the reliability, concurrent validity, and error of a new video digitizing system for evaluating posture when applied to inanimate objects. DESIGN: Delayed repeated measures of digital images of inanimate objects. SETTING: University laboratory. METHODS: Digital video images of inanimate objects (5 parallelograms) of different sizes and shapes were obtained with the BioTonix postural evaluation system. Three examiners digitized video images of inanimate objects twice; the second data collection was 1 week after the first set. The objects were digitized with both high- and low-resolution settings of the video screen. The Tonix's measurements were statistically compared with the actual object dimensions. Statistical evaluations of reliability and validity were conducted. RESULTS: For distances, both intraclass and interclass correlation coefficients were very high, 0.99 for the estimate. The low- versus high-resolution settings were comparable for distances. For angles, on the low-resolution setting, both intraclass and interclass correlation coefficients were very high: 0.969 and 0.953. On the high-resolution setting, for angles, both intraclass and interclass coefficients were well above 0.99. The difference of the actual size and the means of the digitized measurements of the means were small: at most 1.5 degrees for angles and 3.3 mm for distances. The standard deviations were small, and the confidence intervals were narrow. CONCLUSIONS: Our results demonstrate that the BioTonix's video system has high degrees of reliability and validity. Thus this system would seem suitable for clinical use in the analysis of posture.
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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 | 0.016 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".