FIVE-L Classification of Bone Flap Handling in Decompressive Craniectomy
Bibliographic record
Abstract
Decompressive craniectomy (DC) is a well-recognized intervention for the management of elevated intracranial pressure following severe traumatic brain injury, stroke, or other causes of malignant cerebral edema.[ 1 ] [ 2 ] [ 3 ] An important intraoperative consideration is how to handle the bone flap after removal.[ 4 ] Current strategies vary widely based on institutional resources, surgeon preference, and patient-specific factors.[ 5 ] [ 6 ] A novel classification system FIVE-L to standardize bone flap handling strategies, improve intraoperative decision-making, and support surgical education can help in following the patients. The FIVE-L classification has five grades (L1 to L5) and each one represents specific strategy of bone handling ([ Fig. 1 ], [ Table 1 ]). Table 1 FIVE-L classification: bone flap handling strategies Grade Strategy Notes L1 Leave in situ Rarely used; associated with higher infection risk if skin integrity is compromised. May be used in selected cases where swelling is minimal L2 Lock in abdomen Subcutaneous abdominal storage; low-cost, biologically safe; risk of resorption or infection at storage site. Common in resource-limited settings L3 Laboratory freeze Cryopreservation in sterile bone bank; reduces infection risk but requires specialized infrastructure. Often preferred in high-income settings L4 Lose (discard) Reserved for contaminated or necrotic bone. Followed by delayed cranioplasty with synthetic material L5 Load implant Immediate synthetic cranioplasty using PEEK, PMMA, or titanium. Avoids second surgery but increases cost and operative time Abbreviations: PEEK, polyetheretherketone; PMMA, polymethylmethacrylate. Fig. 1 FIVE-L classification: bone flap handling. This classification can be implemented intraoperatively as a decision-making guide and retrospectively to categorize DC procedures for research, auditing, or quality improvement purposes. The FIVE-L classification provides a practical approach to bone flap management in DC. In addition, it allows neurosurgeons to select an appropriate strategy based on patient condition, infection risk, infrastructure, and available materials. It also facilitates retrospective research, surgical audit, and the development of institutional protocols. In other words, each strategy has advantages and limitations. For example, while laboratory freezing (L3) offers excellent sterility, it is not always feasible in low-resource settings, where locking the flap in the abdomen (L2) may be more appropriate. Furthermore, an immediate implantation with synthetic materials (L5) is optimal in select cases but requires careful patient selection and additional resources. We believe that implementing the FIVE-L classification is a practical tool for intraoperative decision-making and postoperative planning in DC. It supports safer, evidence-based, and globally adaptable neurosurgical practice. Publication History Article published online: 22 September 2025 © 2025. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/) Thieme Medical and Scientific Publishers Pvt. Ltd. A-12, 2nd Floor, Sector 2, Noida-201301 UP, India
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".