Skeletal Deformity in Patients With Unilateral Coronal Craniosynostosis: Perceptions of the General Public
Bibliographic record
Abstract
Study Design: A two-alternative forced choice design was used to gather perceptual data regarding unicoronal synostosis (UCS). Objective: Cranial vault remodeling aims at improving the aesthetic appearance of infants with UCS by reshaping the forehead and reducing the potential for psychosocial discrimination. People's perception of craniofacial deformity plays a role in the stigma of deformity. The purpose of this study is to examine the relationship between objective skull deformity in UCS patients and laypersons’ perception of skull normality. Methods: Forty layperson skull raters were recruited from the general public. Skull raters were asked to categorize 45 infant skull images as normal or abnormal. Twenty-one of the images were UCS skulls, and 24 were normal skulls. Skulls were displayed briefly on a computer to simulate a first impression scenario and generate a perceptual response. A χ 2 analysis and mixed-effects regression model were used to analyze the response data. Results: Members of the general public were good at distinguishing between skull groups, χ 2 (1) = 281.97, P < .001. In addition, skull raters’ responses were predicted by the severity of deformity in the UCS skulls ( b = −0.10, z = −2.6, P = .010, CI: −0.18, −0.02). A skull with a deformity value of 2.8 mm (CI: 1.8, 4.1) was equally likely to be rated normal or abnormal. Conclusions: This is the first study to investigate the relationship between objective skull deformity in UCS and public perception. Laypersons were good at distinguishing the difference between normal and UCS skulls, and their perceptions of normality were predicted by the degree of skull deformity.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".