Bibliographic record
Abstract
In this issue of BMJ Quality & Safety , Meddings et al 1 report the evaluation of a national effort to reduce two well-known safety targets, central line-associated bloodstream infection (CLABSI) and catheter-associated urinary tract infection (CAUTI). The paper’s introduction helpfully informs readers of the context. Prior projects funded by the US Agency for Healthcare Research and Quality (AHRQ) have reported well-known successes for both these targets.2 3 One national collaborative reported a greater than 40% reduction in CLABSI in intensive care units (ICUs).2 And, a comparably large project reported a 32% reduction in CAUTI in clinical units other than ICUs, but with no reduction occurring in ICUs.3 This lack of improvement for CAUTI in ICUs might perplex those familiar with the history of these interventions. The AHRQ On the CUSP: Stop CAUTI project3 included the Comprehensive Unit-based Safety Program (CUSP) to support behavioural and cultural changes seen as crucial to support uptake of the technical elements of the CLABSI bundle4 and other checklist-type interventions.5 Why would an intervention for CAUTI modelled after one which has apparently worked so well for CLABSI in ICUs2 6 work only outside ICUs? This unexpected result, along with the fact that, even in the seemingly more successful CLABSI project2 a substantial proportion of ICUs did not improve, led to the national collaborative now reported by Meddings et al .1 The programme recruited 366 ICUs from 220 US hospitals, with 274 ICUs providing complete data. Neither target showed significant improvements. For CLABSI, the incidence rate ratio (IRR) was 0.75, but the 95% CI extended up to an increase of 1.08 (p=0.13). CAUTI showed a similar result: IRR=0.79 but with a CI extending up to 1.06.1 Moreover utilisation for both catheters decreased only marginally and non-significantly. The authors …
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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 | 0.035 | 0.120 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.023 | 0.032 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.038 | 0.051 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".