Systematic Review of the Diagnostic Accuracy of Haptoglobin, Serum Amyloid A, and Fibrinogen versus Clinical Reference Standards for the Diagnosis of Bovine Respiratory Disease
Bibliographic record
Abstract
BACKGROUND: Bovine respiratory disease (BRD) is a worldwide animal health concern especially in feedlot, dairy, and veal calves. One of the greatest challenges is the absence of a gold standard for achieving an accurate antemortem diagnosis. Various blood markers, including the acute-phase proteins (AAP), have been proposed as potential valuable tools for BRD diagnosis. OBJECTIVES: To perform a systematic review of the literature to assess the accuracy of selected APP (haptoglobin [Hp], serum amyloid A [SAA], and fibrinogen [Fb]) as diagnostic tools for cattle with naturally occurring BRD when compared with clinical reference standards of diagnosis. METHODS: This review was performed with eligible studies selected from CAB Abstract and MEDLINE from 1946 to 2015, as well as the "gray literature." Methodological quality of included studies was assessed using the QUADAS-2 tool developed for diagnostic accuracy studies. The accuracy parameters sensitivity (Se) and specificity (Sp) were obtained from the articles or through contact with the authors when not directly reported. RESULTS: A total of 314 studies were identified, from them, 23 met inclusion criteria as diagnostic studies for naturally occurring BRD. Quality of studies showed high risk of bias for case selection (70% of articles) and unclear risk of bias for index test (65%), reference standard (74%), and flow and timing (61%). There were high concerns regarding applicability for case selection (61% of studies) and reference standards used for defining BRD (48%). The concerns regarding index test application were low (83% of the studies). Only 4-8 studies could be included in the meta-analysis for each APP. No pooled estimates or pooled accuracy measurements were performed due to the low number of studies and multiple differences between studies, including reference standard definitions. CONCLUSIONS AND CLINICAL IMPORTANCE: On the basis of these findings, it is not possible to make conclusions regarding the accuracy of APP for BRD diagnosis. The reporting of accuracy of APP for BRD detection is inconsistent among studies. Recommendations to improve capability for future meta-analyses in this area include reporting studies on diagnostic tests following the Standard for the Reporting of Diagnostic Accuracy Studies (STARD), as well as trying to standardize BRD definition across future studies.
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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 | 0.018 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.009 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".