Environmental organisms from different hospital wards
Why this work is in the frame
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Bibliographic record
Abstract
T hree clinical wards in the same hospital, High Dependency (HDU), Care of the Elderly (CE) and Acute Psychiatry (AP), were screened for environmental organisms. The screening programme ran for four weeks and targeted comparable sites from all three units. Floor areas were similar, as were staffing levels, cleaning schedules, infection control policies and patient numbers. Organisms such as coagulase-negative staphylococci, Bacillus spp., coliforms, oxidase-positive Gram-negative bacilli, Clostridium difficile, enterococci, various fungi and Staphylococcus aureus were isolated. Staphylococci and Gram-negative bacilli were tested against clinically appropriate antibiotics. There was little variation in diversity or density of organisms from any of the wards, except for significant differences in antibiotic susceptibilities of the organisms (P<0.0001 HDU v AP, P=0.0057 CE v AP and P=0.0365 HDU v CE). From HDU, 49% (of 43) isolates were resistant to four or more antibiotics and from CE, 37% (of 54) isolates were resistant to four or more. From AP, just 2% (of 52) were resistant to four or more antibiotics. 9% HDU organisms were fully susceptible, as compared with 20% of those from CE and 27% from AP. Antibiotic data (in Defined Daily Doses (g)/100 bed-days) showed that HDU consumed over 12 times more antibiotics than CE, which in turn consumed twice as much as AP; these were mostly intravenous broad-spectrum agents for HDU, as opposed to oral preparations for the other two wards (Chi-square for each ward for linear trends by level of antibiotic intake were all P<0.0001). It was concluded that the only significant difference between environmental bacteria from wards of varying specialities in this hospital is their resistance to antibiotics. Heavy use of antibiotics in a hospital unit, as demonstrated by antibiotic consumption data, may be associated with increased antibiotic resistance in environmental organisms originating from that unit.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 it