The diagnostic value of Red Flags in thoracolumbar pain: a systematic review
Notice bibliographique
Résumé
Red Flags (RFs) are signs and symptoms related to the screening of serious underlying pathologies mimicking a musculoskeletal pain. The current literature wonders about the usefulness of RFs, due to high false-positive rates and low diagnostic accuracy. The aims of this systematic review are: (a) to identify and (b) to evaluate the most important RFs that could be found by a health care professional during the assessment of patients with low and upper back pain (named as thoracolumbar pain (TLP)) to screen serious pathologies. A systematic review of the literature was conducted. Searches were performed on seven databases (Pubmed, Web of Science, Cochrane Library, Pedro, Scielo, CINAHL, and Google Scholar) between March 2019 and June 2020, using a search string which included synonyms of low back pain (LBP), chest pain (CP), differential diagnosis, RF, and serious disease. Only observational studies enrolling patients with LBP or CP were included. Risk of bias was assessed with the Newcastle Ottawa Scale and inter-rater agreement between authors for full-text selection was evaluated with Cohen’s Kappa. Where possible the diagnostic accuracy was recorded for sensitivity (Sn), specificity (Sp), and positive/negative likelihood ratio (LR+/LR–). Forty full-texts were included. Most of the included observational studies were judged as low risk of bias, and Cohen’s Kappa was good (=0.78). The identified RFs were: advanced age; neurological signs; history of trauma; malignancy; female gender; corticosteroids use; night pain; unintentional weight loss; bladder or bowel dysfunction; loss of anal sphincter tone; saddle anaesthesia; constant pain; recent infection; family or personal history of heart or pulmonary diseases; dyspnoea; fever; postprandial CP; typical reflux symptoms; haemoptysis; sweating; pain radiated to upper limbs; hypotension; retrosternal pain; exertional pain; diaphoresis; and tachycardia. The diagnostic accuracy of RFs as self-contained screening tool was low, while the combination of multiple RFs showed to increase the probability to identify serious pathologies. Despite the use of single RF should not be recommended for the screening process in clinical practice, the combination of multiple RFs to enhance diagnostic accuracy is promising. Moreover, the identified RFs could be a baseline to develop a screening tool for patients with TLP.Implications for rehabilitationDifferential diagnosis and screening for referral are mandatory skills for each healthcare professional in direct access clinical settings, and should be the primary step for an appropriate management of a patient with signs and symptoms mimicking serious pathologies in thoracolumbar region.Clinical reasoning and decision-making processes are essential throughout all phases of a patient’s pathway of care. By which, the use of single Red Flag (RF) as a self-contained screening tool should not be recommended. The combination of multiple RFs promises to increase diagnostic accuracy and could grow into an excellent screening tool for thoracolumbar pain. Differential diagnosis and screening for referral are mandatory skills for each healthcare professional in direct access clinical settings, and should be the primary step for an appropriate management of a patient with signs and symptoms mimicking serious pathologies in thoracolumbar region. Clinical reasoning and decision-making processes are essential throughout all phases of a patient’s pathway of care. By which, the use of single Red Flag (RF) as a self-contained screening tool should not be recommended. The combination of multiple RFs promises to increase diagnostic accuracy and could grow into an excellent screening tool for thoracolumbar pain.
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 | 0,002 | 0,126 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 0,003 |
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 ».