0606 - FishCuTv2: An Extensible Software for microCT-Based Whole-Body Skeletal Phenomics in Zebrafish
Notice bibliographique
Résumé
INTRODUCTION: Periprosthetic joint infection (PJI) remains a serious complication of total joint replacement surgery entailing high cost and poor quality of life in patients with joint disease. Clinicians may use a combination of tests to diagnose PJI. We aimed to conduct the first systematic review to identify combinations of tests in diagnosing PJI.METHODS: We conducted the first ever systematic review that assessed existing combination index tests in serum, synovial and tissue for diagnosing PJI in patients with suspected hip and knee infection. This work was part of a larger systematic review based on a published protocol. We search MEDLINE, Embase, Cochrane Library and grey literature, and screened the studies in an online systematic review software program (Distiller Systematic Review (DSR) Softwareu00a9) at two levels. Level was based on title and abstract, and level two based on full text. We used QUADAS-2 tool for assessing the quality of the included studies. The screening and quality assessment were performed independently by two reviewers, and disagreements between reviewers were resolved through consensus or third-party adjudication. This larger review was registered with PROSPERO (registration number: CRD42015023768).RESULTS SECTION: We screened 10526 bibliographic records after removing duplicates at level 1 and excluded 9395. Of 1131 records passed to level 2, 973 were excluded for not meeting the eligibility criteria and 158 were included. Of these, 25 reported combination tests and were included in this report. We identified diagnostic accuracy performance on 35 combination tests. Data for seven combinations originated from at least two studies and for the remaining 28 combinations from single studies. We identified four types of combinations of two tests: 1) type I u201cAND / ORu201d logic: sensitivity = P(both tests positive ' disease positive), specificity = P(either test negative ' disease negative), 2) type II u201cAND / ORu201d logic: sensitivity = P(either test positive ' disease positive), specificity = P(both tests negative ' disease negative), 3) conditional logic: the result of a test conditional on the result of another test or combination, 4) arithmetic rules: arithmetic operations of the value of two tests larger than a threshold. Combinations using type I or II u201cAND /ORu201d logic are not likely to be u201call-perfectu201d. They involve trade-offs: type I results in higher (or no lower) specificity at the cost of lower (or no higher) sensitivity; type II results in higher (or no lower) sensitivity at the cost of lower (or no higher) specificity; however, it is still possible to see one improved while the other does not drop or does not drop a lot.DISCUSSION: Combining diagnostic tests for PJI can improve sensitivity or specificity if certain logical combinations are utilized. Combination of two tests using the u201cAND / ORu201d logic cannot simultaneously improve both sensitivity and specificity to a greater value than the higher sensitivity / specificity of the two tests in a single study. Clinicians could use type I u201cAND / ORu201d logic if aiming for higher specificity but have no concerns over sensitivity or use type II if aiming for higher sensitivity but have no concerns over specificity. Combination of tests based on conditional logic and arithmetic rules need future research.SIGNIFICANCE/CLINICAL RELEVANCE: This is the first ever systematic review that assessed existing combination index tests in serum, synovial and tissue for diagnosing PJI in patients with suspected hip and knee infection. The findings from this review reveal the inconsistencies and biases that exist within the PJI diagnostic literature and highlight the importance of considering all test outcomes when analyzing combination test performances.ACKNOWLEDGEMENTS: We acknowledge Raymond Daniel with help on processing literature search, Roxanne Ward for her administrative support, and Paul Ioudovski for data extraction in the project.
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 | 0,011 | 0,024 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,003 | 0,006 |
| Bibliométrie | 0,008 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,116 | 0,010 |
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 ».