Outcomes of surgical management and implant consideration for depressed skull fractures: A systematic review
Bibliographic record
Abstract
Background: Traumatic brain injuries (TBIs) are associated with high mortality and morbidity. Depressed skull fractures (DSFs) are a subset of fractures characterized by either direct or indirect brain damage, compressing brain tissue. Recent advances in implant use during primary reconstruction surgeries have shown to be effective. In this systematic review, we assess differences in titanium mesh, polyetheretherketone (PEEK) implants, autologous pericranial grafts, and methyl methacrylate (PMMA) implants for DSF treatment. Methods: A literature search was conducted in PubMed, Scopus, and Web of Science from their inception to September 2022 to retrieve articles regarding the use of various implant materials for depressed skull fractures. Inclusion criteria included studies specifically describing implant type/material within treatment of depressed skull fractures, particularly during duraplasty. Exclusion criteria were studies reporting only non-primary data, those insufficiently disaggregated to extract implant type, those describing treatment of pathologies other than depressed skull fractures, and non-English or cadaveric studies. The Newcastle-Ottawa Scale was utilized to assess for presence of bias in included studies. Results: Following final study selection, 18 articles were included for quantitative and qualitative analysis. Of the 177 patients (152 males), mean age was 30.8 years with 82% implanted with autologous graft material, and 18% with non-autologous material. Data were pooled and analyzed with respect to the total patient set, and additionally stratified into those treated through autologous and non-autologous implant material.There were no differences between the two cohorts regarding mean time to encounter, pre-operative Glasgow coma scale (GCS), fracture location, length to cranioplasty, and complication rate. There were statistically significant differences in post-operative GCS (p < 0.0001), LOS (p = 0.0274), and minimum follow-up time (p = 0.000796). Conclusion: Differences in measurable post-operative outcomes between implant groups were largely minimal or none. Future research should aim to probe these basic results deeper with a larger, non-biased sample.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".