Risk factors for positional plagiocephaly and appropriate time frames for prevention messaging
Bibliographic record
Abstract
OBJECTIVE: To determine potential risk factors for developing positional plagiocephaly in infants seven to 12 weeks of age in Calgary, Alberta. METHODS: A prospective cohort design was used. Healthy term infants (n=440), seven to 12 weeks of age, from well-child clinics at four community health centres in Calgary, Alberta were assessed by the primary author and a registered nurse research assistant using Argenta's plagiocephaly assessment tool. Parents completed a questionnaire surveying risk factors. RESULTS: The incidence of positional plagiocephaly was estimated to be 46.6%. The following risk factors were identified using multiple logistic regression: right-sided head positional preference (OR 4.66 [95% CI 2.85 to 7.58]; P<0.001), left-sided head positional preference (OR 4.21 [95% CI 2.45 to 7.25]; P<0.001), supine sleep position (OR 2.67 [95% CI 1.58 to 4.51]; P<0.001), vacuum/forceps assisted delivery (OR 1.88 [95% CI 1.02 to 3.49]; P=0.04) and male sex (OR 1.55 [95% CI 1.00 to 2.38]; P=0.05). CONCLUSION: Advice to vary infants' head positions needs to be communicated to parents/guardians well before the two-month well-child clinic visit. This could occur in the prenatal period by prenatal care providers or educators, or during the neonatal period by postpartum and public health nurses. Prevention education may be emphasized for parents/guardians of male infants and infants who have had assisted deliveries.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".