Practical Guidance for Clinical Microbiology Laboratories: Microbiologic diagnosis of implant-associated infections
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.006 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it