Notice bibliographique
Résumé
CONTEXT: Outpatients with suspected deep vein thrombosis (DVT) have nonspecific signs and symptoms. Missed DVT diagnosis may result in fatal pulmonary embolism. Since many patients may have DVT, a selective and efficient diagnostic process is needed. OBJECTIVE: To systematically review trials that determined the prevalence of DVT using clinical prediction rules either with or without D-dimer, for the diagnosis of DVT. DATA SOURCES: English- and French-language studies were identified from MEDLINE from 1990 to July 2004 and supplemented by a review of all relevant bibliographies. STUDY SELECTION: We included studies that prospectively enrolled consecutive, unselected outpatients with suspected DVT and applied clinical prediction rules before D-dimer testing or diagnostic imaging. All studies included sufficient information to allow the calculation of the prevalence of DVT for at least 1 of the 3 clinical probability estimates (low, moderate, or high). We required that patients be followed up for a minimum 3-month period. Unless the clinical model incorporated prior DVT, studies were excluded if patients with a history of prior DVT were enrolled. DATA EXTRACTION: Two reviewers independently reviewed and abstracted data for estimating the prevalence of DVT, sensitivity, specificity, and likelihood ratios (LRs) of D-dimer in each of the 3 clinical probability estimates. Data for the D-dimer in all studies were pooled and analyzed as high-sensitivity/low-specificity test or a moderate-sensitivity/moderate-specificity test. DATA SYNTHESIS: Fourteen studies involving more than 8000 patients used 1 clinical prediction rule for diagnosing DVT, of which 11 incorporated D-dimer testing in the diagnostic algorithm. The prevalence of DVT in the low, moderate, and high clinical probability groups was 5.0% (95% CI, 4.0%-8.0%), 17% (95% CI, 13%-23%), and 53% (95% CI, 44%-61%), respectively. The overall prevalence of DVT was 19% (95% CI, 16%-23%). Pooling all studies, the sensitivity, specificity, and negative LRs of D-dimer testing in the low probability group were 88% (95% CI, 81%-92%), 72% (95% CI, 65%-78%), and 0.18% (95% CI, 0.12-0.18); in the moderate probability group: 90% (95% CI, 80%-95%), 58% (95% CI, 49%-67%), and 0.19% (95% CI, 0.11-0.32); and in the high probability group: 92% (95% CI, 85%-96%), 45% (95% CI, 37%-52%), and 0.16% (95% CI, 0.09-0.30). The LRs for a normal result on a high or moderately sensitive D-dimer assay among patients with: (1) low clinical suspicion were 0.10 (95% CI, 0.03-0.37) and 0.20 (95% CI, 0.12-0.31); (2) moderate clinical suspicion were 0.05 (95% CI, 0.01-0.21) and 0.23 (95% CI, 0.13-0.39); and (3) high clinical suspicion were 0.07 (95% CI, 0.03-0.18) and 0.15 (95% CI, 0.10-0.38). CONCLUSIONS: Diagnostic accuracy for DVT improves when clinical probability is estimated before diagnostic tests. Patients with low clinical probability on the predictive rule have prevalence of DVT of less than 5%. In low-probability patients with negative D-dimer results, diagnosis of DVT can be excluded without ultrasound; in patients with high clinical suspicion for DVT, results should not affect clinical decisions.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».