Bibliographic record
Abstract
Antibiotic consumption and population density as a measure of crowding in the community were related to the prevalence of antibiotic resistance of three cities in three different countries: St Johns in Newfoundland (Canada), Athens in Greece and Groningen in The Netherlands. Antibiotic consumption was expressed in DDD (defined daily dose), as DID (DDD/1000 inhabitants/day) and as DSD (DDD/km(2)). The prevalence of antibiotic-resistant Escherichia coli and enterococci was determined in faecal samples of healthy volunteers. In both Newfoundland (28 DID) and Greece (29 DID) the overall consumption of antibiotics was more than three times higher compared with that of The Netherlands (9 DID). The lowest prevalence of resistant E. coli against the majority of antibiotics tested was found for the samples from Newfoundland and was significant (P < 0.05) for cefazolin, oxytetracycline and trimethoprim. A poor correlation between the number of DID and the prevalence of resistance was observed [the Pearson correlation coefficient (Pcc) ranged between -0.93 and 0.87]. However, when population density was taken into consideration and antibiotic consumption was expressed in DSD, a strong correlation was observed (and Pcc ranged between 0.86 and 1.00). This study suggests that population density is an important factor in the development of antibiotic resistance and warrants special attention as a factor in resistance epidemiology.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".