Sinking skin flap syndrome in a patient with bone resorption after cranioplasty and ventriculoperitoneal shunt placement: illustrative case
Bibliographic record
Abstract
BACKGROUND: Sinking skin flap syndrome (SSFS) is an uncommon complication that can follow decompressive craniectomy. Even less common is the development of SSFS following bone resorption after cranioplasty with exacerbation by a ventriculoperitoneal (VP) shunt. OBSERVATIONS: A 56-year-old male sustained a severe traumatic brain injury and subsequently underwent an emergent decompressive craniectomy. After craniectomy, a cranioplasty was performed, and a VP shunt was placed. The patient returned to the emergency department 5 years later with left-sided hemiplegia and seizures. His clinical presentation was attributed to complete bone flap resorption (BFR) complicated by SSFS likely exacerbated by his VP shunt and the resultant mass effect on the underlying brain parenchyma. The patient underwent surgical intervention via synthetic bone flap replacement. Within 6 days, he recovered to his baseline neurological status. LESSONS: SSFS after complete BFR is a rare complication following cranioplasty. To the authors' knowledge, having a VP shunt in situ to exacerbate the clinical picture has yet to be reported in the literature. In addition to presenting the case, the authors also describe an effective treatment strategy of decompressing the brain and elevating the scalp flap while addressing the redundant tissue, then using a synthetic mesh to reconstruct the calvarial defect while keeping the shunt in situ.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".