Innovative Solutions for Patients Who Undergo Craniectomy: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: Decompressive craniectomy (DC) is a widely used procedure to alleviate high intracranial pressure. Multidisciplinary teams have designed and implemented external medical prototypes to improve patient life quality and avoid complications following DC in patients awaiting cranioplasty (CP), including 3D printing and plaster prototypes when available. OBJECTIVE: This scoping review aims to understand the extent and type of evidence about innovative external prototypes for patients who undergo DC while awaiting CP. METHODS: This scoping review will use the Joanna Briggs Institute methodology for scoping reviews. This scoping review will include noninvasive medical devices for adult patients who undergo DC while waiting for CP. The search strategy will be implemented in MEDLINE, Embase, Web of Science, Scielo, Scopus, and the World Health Organization (WHO) Global Health Index Medicus. Patent documents were also allocated in Espacenet, Google Patents, and the World Intellectual Property Organization (WIPO) database. RESULTS: This scoping review is not subject to ethical approval as there will be no involvement of patients. The dissemination plan includes publishing the review findings in a peer-reviewed journal and presenting results at conferences that engage the most pertinent stakeholders in innovation and neurosurgery. CONCLUSIONS: This scoping review will serve as a baseline to provide evidence for multidisciplinary teams currently designing these noninvasive innovations to reduce the risk of associated complications after DC, hoping that more cost-effective models can be implemented, especially in low- and middle-income countries. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/50647.
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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 | 0.049 | 0.049 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.014 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.093 | 0.014 |
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".