The Argenta Classification for Positional Plagiocephaly in Infants: An Inter- and Intra-Rater Reliability Study
Bibliographic record
Abstract
Positional plagiocephaly is a common condition in infants, characterized by asymmetrical posterior occipital flattening due to external mechanical pressure. The Argenta classification is an observational diagnostic tool that classifies plagiocephaly into five degrees of severity according to specific cranial asymmetry characteristics. The purpose was to examine the inter-rater and intra-rater reliability of the Argenta classification among developmental physiotherapists. Two raters examined 42 infants aged 6 weeks to 12 months that were separately enrolled in Clalit child development centers. A second observation was held within 7 days of the first observation. A strong agreement was reached among the raters (κ = 0.85, p < 0.0001) and within two observations of the same rater (κ = 0.90, p < 0.0001). The high degree of agreement indicates the high reliability of the Argenta classification. No significant relationships between severity and gender, age, flattened side, primaparity, number of pregnancies, type of birth, or the Alberta Infant Motor Scale score were found. In conclusion, the Argenta classification demonstrates high inter- and intra-rater reliability, particularly for milder severity levels. It is an easy, quick, and free method to use clinically without causing inconvenience to the assessed infant. While these results support clinical integration, particularly in resource-limited settings, the variable reliability across severity levels indicate that further validation studies are needed before universal adoption.
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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 | 0.036 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".