Neaneurizminės kilmės spontaninės subarachnoidinės hemoragijos stebėjimo strategija
Bibliographic record
Abstract
Karla Margarete Hennigs. Follow-up Strategy of Non-aneurysmal Subarachnoid Hemorrhage. Aim. To investigate the rationale of follow-up strategy of non-aneurysmal subarachnoid hemorrhages. Objectives. 1) To determine the distribution of blood in a subarachnoid hemorrhage on initial non-contrast CT and its correlation of unfavorable outcome for patients with non-aneurysmal subarachnoid hemorrhage; 2) To identify the occurrence of intracranial aneurysms on follow-up imaging for patients with non-aneurysmal subarachnoid hemorrhage; 3) To compare different neurovascular imaging modalities by its ability to detect aneurysms on follow-up as the cause of previously detected subarachnoid hemorrhage with negative angiography; 4) To determine the accurate timing for follow-up investigations to detect intracranial aneurysm for patients with non-aneurysmal subarachnoid hemorrhage. Methods. A systematic literature review was conducted following the PRISMA guidelines. A systematic data search on PubMed was performed, guided by inclusion criteria structured around the PICO paradigm, leading to the inclusion of 26 reports. The Newcastle-Ottawa Quality Assessment Scale was employed to assess the quality of scientific articles with a control group, while the JBI Critical Appraisal Checklist for Systematic Reviews and Research Syntheses was utilized for systematic reviews. Results. The most employed classification is the differentiation between PM-SAH and NP-SAH subarachnoid hemorrhage. NP-SAH is associated with increased rates of complications, mortality, and unfavorable outcomes, necessitating more comprehensive management strategies. Studies show variable rates of aneurysm detection on follow-up imaging, with a mean of 9.7%. The predominant imaging modality employed was DSA, utilized in 13 out of 16 studies, identifying an aneurysm in 9.1% of cases. MRI/A was the second most frequently employed modality, featured in 7 out of 16 studies, with an aneurysm detection rate of 1.9%. Follow-up imaging within the initial two weeks had the highest likelihood of detecting an aneurysm, with a mean diagnostic yield of 14.65%, compared to 10.53% between 2 – 8 weeks and 1.23% after eight weeks. Conclusions. 1) There is significant heterogeneity in the classification of blood distribution patterns on initial CT scans. A standardized classification system could aid in the detection of aneurysms in highly suspicious bleeding patterns while also reducing the need for unnecessary imaging in cases where the probability of identifying a bleeding source is minimal.; 2) Idiopathic SAH causes remain uncertain, with theories suggesting microaneurysm self-repair, venous hemorrhage, and occult aneurysms. Detecting occult aneurysms through follow-up imaging is crucial for preventing recurrent hemorrhage and improving outcomes.; 3) Despite its gold standard status, concerns about DSA's invasiveness prompt exploration of non-invasive options like CTA and MRI/A. Size considerations for aneurysms are crucial, as some modalities may have lower sensitivity. While 3D imaging techniques show potential, complications and risk-benefit ratios of repeat DSA should be considered.; 4) Many studies lack clarity on imaging timing, with a trend towards repeating angiographic exams after a 14-day interval. However, recent studies suggest an earlier follow-up within one to two weeks after the initial event, as supported by our results.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".