Practical Guidance for Clinical Microbiology Laboratories: Microbiologic diagnosis of implant-associated infections
Bibliographic record
Abstract
SUMMARYImplant-associated infections (IAIs) pose serious threats to patients and can be associated with significant morbidity and mortality. These infections may be difficult to diagnose due, in part, to biofilm formation on device surfaces, and because even when microbes are found, their clinical significance may be unclear. Despite recent advances in laboratory testing, IAIs remain a diagnostic challenge. From a therapeutic standpoint, many IAIs currently require device removal and prolonged courses of antimicrobial therapy to effect a cure. Therefore, making an accurate diagnosis, defining both the presence of infection and the involved microorganisms, is paramount. The sensitivity of standard microbial culture for IAI diagnosis varies depending on the type of IAI, the specimen analyzed, and the culture technique(s) used. Although IAI-specific culture-based diagnostics have been described, the challenge of culture-negative IAIs remains. Given this, molecular assays, including both nucleic acid amplification tests and next-generation sequencing-based assays, have been used. In this review, an overview of these challenging infections is presented, as well as an approach to their diagnosis from a microbiologic perspective.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.019 |
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".