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Record W4376125965 · doi:10.1016/j.bas.2023.101760

Infection-related failure of autologous versus allogenic cranioplasty after decompressive hemicraniectomy – A systematic review and meta-analysis

2023· review· en· W4376125965 on OpenAlexaboutno aff
Tiphaine Cerveau, Tobias Rossmann, Hans Clusmann, Michael Veldeman

Bibliographic record

VenueBrain and Spine · 2023
Typereview
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsnot available
Fundersnot available
KeywordsCranioplastyMedicineDecompressive craniectomySurgeryMeta-analysisTraumatic brain injuryInternal medicineSkull

Abstract

fetched live from OpenAlex

Cranioplasty is required after decompressive craniectomy (DC) to restore brain protection and cosmetic appearance, as well as to optimize rehabilitation potential from underlying disease. Although the procedure is straightforward, complications either caused by bone flap resorption (BFR) or graft infection (GI), contribute to relevant comorbidity and increasing health care cost. Synthetic calvarial implants (allogenic cranioplasty) are not susceptible to resorption and cumulative failure rates (BFR and GI) tend therefore to be lower in comparison with autologous bone. The aim of this review and meta-analysis is to pool existing evidence of infection-related cranioplasty failure in autologous versus allogenic cranioplasty, when bone resorption is removed from the equation. A systematic literature search in PubMed, EMBASE, and ISI Web of Science medical databases was performed on three time points (2018, 2020 and 2022). All clinical studies published between January 2010 and December 2022, in which autologous and allogenic cranioplasty was performed after DC, were considered for inclusion. Studies including non-DC cranioplasty and cranioplasty in children were excluded. The cranioplasty failure rate based on GI in both autologous and allogenic groups was noted. Data were extracted by means of standardized tables and all included studies were subjected to a risk of bias (RoB) assessment using the Newcastle-Ottawa assessment tool. A total of 411 articles were identified and screened. After duplicate removal, 106 full-texts were analyzed. Eventually, 14 studies fulfilled the defined inclusion criteria including one randomized controlled trial, one prospective and 12 retrospective cohort studies. All but one study were rated as of poor quality based on the RoB analysis, mainly due to lacking disclosure why which material (autologous vs. allogenic) was chosen and how GI was defined. The infection-related cranioplasty failure rate was 6.9% (125/1808) for autologous and 8.3% (63/761) for allogenic implants resulting in an OR 0.81, 95% CI 0.58 to 1.13 (Z ​= ​1.24; p ​= ​0.22). In respect to infection-related cranioplasty failure, autologous cranioplasty after decompressive craniectomy does not underperform compared to synthetic implants. This result must be interpreted in light of limitations of existing studies. Risk of graft infection does not seem a valid argument to prefer one implant material over the other. Offering an economically superior, biocompatible and perfect fitting cranioplasty implant, autologous cranioplasty can still have a role as the first option in patients with low risk of developing osteolysis or for whom BFR might not be of major concern. This systematic review was registered in the international prospective register of systematic reviews. PROSPERO: CRD42018081720.

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 imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.016
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.028
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0160.033
Bibliometrics0.0070.008
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.062
GPT teacher head0.346
Teacher spread0.285 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations14
Published2023
Admission routes1
Has abstractyes

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