Bibliographic record
Abstract
The treatment of positional plagiocephaly is controversial. A confounding factor is the lack of a proven clinically viable measure to quantify severity and change in plagiocephaly. The use of anthropometric measurements is one proposed method. In this study, the reliability and validity for this method of measurement were investigated. Two clinicians independently recorded caliper measurements of cranial vault asymmetry (CVA) for infants referred for plagiocephaly or torticollis, and an unbiased observer recorded visual analysis scores during the same visit. CVA scores were assigned into three predetermined severity categories (normal CVA < 3 mm, mild/moderate CVA 12 mm). CVA measurements and visual analysis scores were recorded for 71 and 54 infants, respectively. Intrarater reliability was established (kappa = 0.98, kappa = 0.99), but inter-rater reliability was not (kappa = 0.42). In addition, the inter-rater reliability for the severity categories based upon these measures was poor (kappa = 0.28) and failed to correlate to the visual analysis (kappa = 0.31). Development of a stable and meaningful measurement system for the extent of plagiocephaly is needed to allow scientific studies of the natural history of plagiocephaly and effectiveness of interventions.
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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".