Measuring Child Length and Height: Assessing the Accuracy of a Portable Infrared‐based Digital Tool
Bibliographic record
Abstract
Accurate measurement of growth in infants and children is important, as it reflects a child's nutritional status and overall health. Conventional methods of measuring length and height are challenging due to limited access to appropriate tools, poor tool quality, and lack of child cooperation. Pediatric Platform for Anthropometry (PePA) is a portable, computer‐assisted infrared‐based tool that measures length/height without direct child contact. This study investigated PePA's accuracy by comparing length/height measures to the gold standard (length board or stadiometer). The precision and acceptability of PePA and the gold standard were also assessed. In a preliminary analysis of 316 eligible children (0‐18 years), 278 participants' PePA and gold standard measures met the study criteria for inclusion in the analysis. For 6% of those eligible, no successful PePA measures were obtained. The overall intraclass correlation (ICC) [95% CI] between PePA and the gold standard was 0.9988 [0.9985, 0.9991]. The ICC for length only (n=16) and height only (n=262) was 0.9840 [0.9286, 0.9951] and 0.9981 [0.9976, 0.9985], respectively. The mean (±SD) time to obtain a PePA measure was less than the standard tool (8.4±3.2 versus 14.1±5.3 seconds, respectively; p<0.05). Preliminary results suggest that PePA has a very high concordance with the gold standard and is faster in obtaining a measure. Research support was provided by a Hospital for Sick Children Innovation Grant.
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 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 | 0.014 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| 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".