Bacterial toxins in musculoskeletal infections
Bibliographic record
Abstract
Musculoskeletal infections (MSKIs) remain a major health burden in orthopaedics. Bacterial toxins are foundational to pathogenesis in MSKI, but poorly understood by the community of providers that care for patients with MSKI, inducing an international group of microbiologists, infectious diseases specialists, orthopaedic surgeons and biofilm scientists to review the literature in this field to identify key topics and compile the current knowledge on the role of toxins in MSKI, with the goal of illuminating potential impact on biofilm formation and dispersal as well as therapeutic strategies. The group concluded that further research is needed to maximize our understanding of the effect of toxins on MSKIs, including: (i) further research to identify the roles of bacterial toxins in MSKIs, (ii) establish the understanding of the importance of environmental and host factors and in vivo expression of toxins throughout the course of an infection, (iii) establish the principles of drug-ability of antitoxins as antimicrobial agents in MSKIs, (iv) have well-defined metrics of success for antitoxins as antiinfective drugs, (v) design a cocktail of antitoxins against specific pathogens to (a) inhibit biofilm formation and (b) inhibit toxin release. The applicability of antitoxins as potential antimicrobials in the era of rising antibiotic resistance could meet the needs of day-to-day clinicians.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".