Neaneurizminės kilmės spontaninės subarachnoidinės hemoragijos stebėjimo strategija
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».