Bibliographic record
Abstract
Systemic Staphylococcal infections were always a dreaded complication of various injuries and for many centuries meant amputation or death for the unfortunate patient. MRSA has become since the 60's a major cause for systemic infections and in various settings are responsible to ∼80% of all nosocomial bacteremias. In other settings, MRSA is responsible ∼50% and in a recent survey of ICU infections among ∼14.000 patients the infection rate had decreased to 47% (Vincent et al JAMA 302;2323:2009). MRSA infections are characterized by various clinical presentations from mild- to rapidly progressing and frequently with involvement of multiple organs includ. heart valvaes, bones, joints and implanted foreign bodies. Those locations, with the particular resistance pattern of MRSA, and only a few effective antibiotics to choose from, make MRSA infections particularly difficult to treat with a frequent need for surgery. The frequent use of foreign bodies in therapy (IV catheters, implants,dialysis catheters, CNS shunts etc’) and the biological avidity of this organism to these foreign materials add to the complexity of the infection. Quorum sensing and the ability of the organism to create a biofilm and to detach from the adherent colony add also to the complexity of these infections. Understanding of these biological mechanisms and being able to interfere should allow for some future therapeutic measures. Evidently, meanwhile, hospital infection control and a possible future staphylococcal vaccine are solutions to peruse. CA MRSA can be controlled by hyginic measures as well as vaccine as this organism despite its low potential to cause systemic infections is likely to stay for many years. Abstracts for SupplementInternational Journal of Infectious DiseasesVol. 14Preview Full-Text PDF Open Archive
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.012 |
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".