Laboratory data as a quality indicator of health-care-associated infections in England
Bibliographic record
Abstract
Routine diagnostic laboratory results, e.g. numbers of meticillin-resistant Staphylococcus aureus (MRSA) bacteraemias, have been used as health-care-associated infection quality indicators for decades. The English health-care-associated infection quality indicator system was one of the earliest in the world to mandate the collection and public reporting of such data and has been associated with a reduction of MRSA bacteraemias and Clostridium difficile infections but has shown mixed results for other infections. Diagnostic laboratory data vary greatly between hospitals depending not only on the underlying frequency of the infection of interest, but on the case mix, numbers of samples processed and laboratory factors, which limits benchmarking. Further, over-reliance on laboratory reports has led to unintended negative consequences in England. So, while acknowledging the successes of the English system, the authors believe that it should be appraised in light of the goals of quality of care, patient safety, fairness and providing meaningful data, and alternative healthcare-associated infection quality indicator measurements considered.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".