Identifying the Pathogen by Multiplex Polymerase Chain Reaction in Bone and Joint Infections: Challenges and Future
Bibliographic record
Abstract
Diagnosis of bone and joint infections remains a challenge. In addition to the difficulty of obtaining sufficient and good clinical samples for microbiological assessment, the bacteria are often few and do not always grow. In these typical biofilm-associated infections, bacteria are embedded in a biofilm matrix, protecting them from the host’s immune system as well as from antibiotics used to treat the infection. In addition, biofilm-associated bacteria are difficult to cultivate because they are not easily recovered from the biofilm, they may be few in number, and they are often in a dormant or slow-growing state. To circumvent these difficulties encountered in recovering the bacteria in classical bacterial cultures, some possibilities that have been discussed are PCR, microcalorimetry, and concentrating the recovered fluids. Aiming to broaden the spectrum and efficiency, multiplex PCR assays have been introduced to identify the causative pathogen in bone and joint infections1,2,3,4. Because bacterial growth is not required, multiplex PCR have been thought to be the solution for diagnosis of culture-negative orthopedic infections in patients who already took antibiotics prior to the diagnostic investigation. All assays promised to have advantages of a rapid test time and to detect a large number of microorganisms, with specific primers also allowing diagnosis of polymicrobial infections. However, to date no commercial assay has found its way into routine practice, mainly because of low sensitivity or the lack of primers for pathogens not included in the multiplex primers kits. In this issue of The Journal , Morgenstern, et al 5 showed results of a prospective … Address correspondence to Dr. Y. Achermann, Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, Raemistrasse 100, CH-8091 Zurich, Switzerland. E-mail: yvonne.achermann{at}usz.ch
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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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".