0606 - FishCuTv2: An Extensible Software for microCT-Based Whole-Body Skeletal Phenomics in Zebrafish
Bibliographic record
Abstract
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.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.116 | 0.010 |
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".