Risk factors for bone flap resorption after autologous bone cranioplasty
Bibliographic record
Abstract
BACKGROUND: One of the most common complications following autologous cranioplasty is bone flap resorption (BFR). Severe BFR can lead to revision surgery with implantation of synthetic bone flap and also necessarily lead to higher hospital expenses. This study aims to perform a meta-analysis to summarize available evidence regarding risk factors of BFR requiring a second surgery in patients with autologous cranioplasty. METHODS: Cohort, case-control, and cross-sectional studies that report the incidence and risk factors of BFR among patients with autologous cranioplasty, published in English, will be considered for selection. Three databases from inception to May 2020 will be searched. The process of data selection, quality assessment, and data extraction will be assessed by 2 authors independently. The study quality will be assessed by Newcastle-Ottawa Scale (NOS) and Agency for Healthcare Research and Quality checklist.The statistical analysis of this meta-analysis will be calculated by Review manager version 5.3. RESULTS: The results of this systematic review and meta-analysis will be disseminated through academic conferences and expected to publish in a peer-reviewed journal CONCLUSION:: This study will offer high-quality evidence about risk factors for BFR after autologous cranioplasty. REGISTRATION NUMBER: INPLASY202050063.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.016 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".