Peer Review #3 of "Comparison of 3D laser-based photonic scans and manual anthropometric measurements of body size and shape in a validation study of 123 young Swiss men (v0.1)"
Bibliographic record
Abstract
Background: Manual anthropometric measurements are time-consuming and challenging to perform within acceptable intra-and inter-individual error margins in large studies.Three-dimensional (3D) laser body scanners provide a fast and precise alternative: Within a few seconds the system produces a 3D image of the body topography and calculates some 150 standardised body size measurements. Objective:The aim was to enhance the small number of existing validation studies and compare scan and manual techniques based on five selected measurements.We assessed the agreement between two repeated measurements within the two methods, analysed the direct agreement between the two methods, and explored the differences between the techniques when used in regressions assessing the effect of health related determinants on body shape indices. Methods:We performed two repeated body scans on 123 volunteering young men using a Vitus Smart XXL body scanner.We manually measured height, waist, hip, buttock, and chest circumferences twice for each participant according to the WHO guidelines.The participants also filled in a basic questionnaire.Results: Mean differences between the two scan measurements were smaller than between the two manual measurements, and precision and intra-class correlation coefficients were higher.Both techniques were strongly correlated.When comparing means between both techniques we found significant differences: Height was systematically shorter by 2.1cm, whereas waist, hip and bust circumference measurements were larger in the scans by 1.17-4.37cm.In consequence, body shape indices also became larger and the prevalence of overweight was greater when calculated from the scans.Between 4.1% and 7.3% of the probands changed risk category from normal to overweight when classified based on the scans.However, when employing regression analyses the two measurement techniques resulted in very similar coefficients, confidence intervals, and p-values. Conclusion:For performing a large number of measurements in a large group of probands in a short time, body scans generally showed good feasibility, reliability, and validity in comparison to manual measurements.The systematic differences between the methods may result from their technical nature (contact vs. non-contact).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".