Quality of life and caregiver outcomes following decompressive hemicraniectomy for severe stroke: a narrative literature review.
Bibliographic record
Abstract
AIMS: The aim of this narrative review of the literature was to examine the current state of knowledge regarding the impact of aggressive surgical interventions for severe stroke on patient and caregiver quality of life and caregiver outcomes. BACKGROUND: Decompressive hemicraniectomy (DHC) is a surgical therapeutic option for treatment of massive middle cerebral artery infarction (MCA), lobar intracerebral hemorrhage (ICH), and severe aneurysmal subarachnoid hemorrhage (aSAH). Decompressive hemicraniectomy has been shown to be effective in reducing mortality in these three life-threatening conditions. Significant functional impairment is an experience common to many severe stroke survivors worldwide and close relatives experience decision-making difficulty when confronted with making life or death choices related to surgical intervention for severe stroke. DATA SOURCES: Academic Search Premier, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Medline, and PsychInfo. REVIEW METHODS: A narrative review methodology was utilized in this review of the literature related to long-term outcomes following decompressive hemicraniectomy for stroke. The key words decompressive hemicraniectomy, severe stroke, middle cerebral artery stroke, subarachnoid hemorrhage, lobar ICH, intracerebral hemorrhage, quality of life, and caregivers, literature review were combined to search the databases. RESULTS: Good functional outcomes following DHC for life-threatening stroke have been shown to be associated with younger age and few co-morbid conditions. It was also apparent that quality of life was reduced for many stroke survivors, although not assessed routinely in studies. Caregiver burden has not been systematically studied in this population. CONCLUSION: Most patients and caregivers in the studies reviewed agreed with the original decision to undergo DHC and would make the same decision again. However, little is known about quality of life for both patients and caregivers and caregiver burden over the long-term post-surgery. Further research is needed to generate information and interventions for the management of ongoing patient and carer recovery following DHC for severe stroke.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".