Growth delay: an alternative measure of population health based on child height distributions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: -scores (HAZ), height-for-age difference (HAD) and stunting prevalence, do not account for differences in population-average bone developmental stage. AIM: Propose a measure of child height that conveys the dependency of linear growth on stage rather than chronological age. SUBJECTS AND METHODS: Using Demographic and Health Surveys (2000-2018; 64 countries), we generated: (1) predicted HAZ at specific ages (HAZ regressed on age); (2) height-age (age at which mean height matches the WHO Growth Standards median); (3) Growth delay (GD), the difference between chronological age and height-age; (4) HAD; and (5) stunting prevalence. Metrics were compared based on secular trends within countries and age-related trajectories within surveys. RESULTS: = 64), GDs ranged from 1.9 to 19.1 months at 60 months chronological age. Cross-sectionally, HAZ, HAD and GD were perfectly correlated, and showed similar secular trends. However, age-related trajectories differed across metrics. Accumulating GD with age demonstrated growth faltering as slower than expected growth for children of the same height-age. Resumption of growth at the median for height-age was rarely observed. CONCLUSION: GD is a population-level measure of child health that reflects the role of delayed skeletal development in linear growth faltering.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it