The first step in an investigation of quantitative ultrasound as a technique for evaluating infant bone strength
Bibliographic record
Abstract
This study's purpose is to evaluate whether bone speed of sound (SOS) data, a parameter of quantitative ultrasound, collected from an infant autopsy sample are comparable to data collected from healthy, living infants. We hypothesize that SOS values obtained from deceased term-born infants will fall within the normal range for healthy, living infants. The study sample consists of 351 deceased infants between the ages of 30 weeks gestation at birth to 1 year postnatal at the time of death receiving autopsies at the Harris County Institute of Forensic Sciences or Texas Children's Hospital in Houston, TX. Various multivariate and univariate statistics were used to examine the relationship between SOS and age, prematurity, and chronic illness. The results of an ANOVA comparing the study sample data to published data from healthy, living infants indicate the SOS data are comparable. Additionally, a MANOVA indicated significant differences in SOS related to prematurity (p = 0.001) and age (p < 0.001). Mean SOS was significantly greater among term-born infants (M = 3065.66, SD =165.05) than premature infants (M = 2969.71, SD =192.72). Age had a significant polynomial (cubic) relationship with SOS for both the premature and term groups (p < 0.001). Results suggest that bone from an infant autopsy sample is an appropriate surrogate to examine the relationship between SOS and determinants of bone strength. Therefore, future research will use this study sample to investigate the relationship between SOS and determinants of bone strength in infants.
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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 | 0.020 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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".