P.186 Trends in the management of scalp aplasia cutis congenita
Bibliographic record
Abstract
Background: Aplasia Cutis Congenita is a rare congenital abnormality characterized by varying absence of skin and mesodermal tissues. Management remains controversial, with significant inconsistency across specialties and over time. This review evaluates trends in management based on journal specialty and publication epoch. Methods: A review of pediatric scalp Aplasia Cutis Congenita management was conducted. Articles were categorized by journal type (neurosurgery, plastic surgery, dermatology, other medical, and other surgical) and management approach (surgical, conservative, or combined). Descriptive statistics were used to summarize trends in recommendations, and assess associations between journal type and treatment. Trends over time were analyzed based on publication year. Results: A total of 171 studies were included. Among surgical journals, 33.7% recommended surgical management, while medical journals favored conservative (14.1%) or combined approaches (84.5%) (p < 0.001). Recommendations for surgical management decreased from 80% in the 1970s to 30% in the 2020s. Notably, among 119 studies advocating for a combined approach, only 27 provided criteria for surgical indications, with lesion thresholds ranging from >1cm to >15 cm. Conclusions: This study highlights the lack of guidelines for ACC management and reveals specialty and time epoch of publication-dependent biases in treatment. These findings emphasize the need for multidisciplinary guidelines for consistent, patient-centered care.
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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 | 0.011 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.022 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".