The Top 100 Most-Cited Publications in Clinical Craniofacial Research
Bibliographic record
Abstract
INTRODUCTION: Craniosynostosis is a birth defect defined as premature closure of sutures leading to possible neurological deficits and cosmetic deformities. Most of the current literature to date focuses on craniosynostosis etiology by analyzing genetics. This paper is a bibliometric analysis of the most influential works related to the clinical management of craniosynostosis to help guide clinicians in their decision-making. METHODS AND MATERIALS: Clarivate Web of Science database was used to identify the top 100 most-cited articles addressing the clinical management of craniosynostosis. A bibliometric review was performed to analyze publication metrics and track research trends. RESULTS: The 100 most-cited publications pertaining to craniosynostosis management were cited a cumulative 12,779 times. The highest cited article was Shillito and colleagues' "Craniosynostosis: A Review Of 519 Surgical Patients" with 352 citations. The oldest clinical craniosynostosis article dates back to 1948, and the most recent was published in 2016. The year with the most clinical-focused publications was 2011. The most prolific author was Renier, D. The United States produced 56 of the 100 articles. Most articles (n=52) were level 3 evidence. DISCUSSION: This bibliometric evaluation of craniosynostosis provides insight into the most impactful literature on this topic. The highest cited articles retrospectively analyze large sample sizes, outline proper evaluation, discuss intervention timelines, and highlight specific treatment plans for this birth defect. By filtering through existing literature, this analysis can guide clinicians on the management of craniosynostosis to maximize patient outcomes.
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 | 0.007 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.117 | 0.143 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".