Prevalence, Severity Patterns and Risk Factors of Bronchopulmonary Dysplasia in Preterm Infants Younger than 32 Weeks of Gestation in a Tertiary Centre in Oman
Bibliographic record
Abstract
Objectives: Cranioplasty is a complex craniofacial and neurosurgical procedure that aims to reinstate the architecture of the cranial vault and elevate both its aesthetic and neurological function. Several reconstructive materials have been thoroughly explored in the search for the optimal solution for cranioplasty. This study aimed to evaluate different material used for cranial reconstruction in Oman. Methods: This retrospective study included all patients who had had cranioplasty procedures performed at Khoula Hospital, Muscat, Oman, from 2012 to 2022. Demographic information, the characteristics of the cranial defect and any complications that occurred post-operatively were analysed. Results: A total of 47 patients were included in this study. The most common cause of cranial defects was craniectomy following traumatic head injury (70.2%) along with excision of fibrous dysplasia (10.6%). The most frequently utilised material for cranial repair was autologous bone grafts (n = 28), followed by polyetheretherketone (PEEK; n = 14). Interestingly, the replacement of bone grafts from previous craniectomy showed a notably high resorption rate (71.4%), in contrast to split calvarial grafts (0%) and other types of bone grafts (14.3%). Additionally, delayed graft infection was observed in 3.6% of the bone graft group and 7.1% of the PEEK group. Conclusion: Patient-specific alloplastic implants such as PEEK have gained popularity for large and complex cranioplasty, as they provide excellent aesthetic outcomes and leave no donor site morbidity. In contrast, bone grafts remain the gold standard for small to medium-sized cranial defects.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".