Bibliographic record
Abstract
Direct microbiological input to critical care is essential for the management of the septic patient. Early broad-spectrum antimicrobial therapy with appropriate diagnostic studies to ascertain causative organisms is well established; there should be reassessment with the aim of using narrow-spectrum antibiotics to prevent the development of antimicrobial resistance, to reduce toxicity and to reduce costs [ 1 ]. In systematic analysis of ward rounds in ICUs the information most commonly missing from a patient's file concerned microbiology findings [ 2 ]. We performed a telephone survey of all NHS critical care units in the North West of England ( n = 31). Each unit was telephoned and the duty consultant was asked a series of questions relating to the type of microbiology input to their critical care unit. We achieved a 100% response rate. The study looked at 11 teaching hospitals and 21 district general hospitals representing 12% of UK ICUs: 26 (83%) critical care units had live computerised access to microbiology data, 21 (68%) units had an antibiotic policy in place, and 19 (61%) units had a formal microbiology ward round. With the frequency ranging from once per week (one unit) to 7 days per week (four units), most units with a microbiology ward round had this service Monday–Friday (12 units). When asked to rate the value of this ward round, the mean score was 8.6 out of a possible 10 (range 10–5, mode 9). In those units without a microbiology ward round the desirability of such a service was scored on average at 8.5 out of 10 (range 10–3, mode 9). Direct microbiological advice at the bedside is highly valued by ICU consultants. Antibiotic prescribing is generally well controlled, with two-thirds of units having an agreed antibiotic policy in place. Work will continue to determine whether these results reflect the national picture in the United Kingdom.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.059 | 0.015 |
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".