Meta-analysis of bacterial resistance to macrolides—providing generalizable results: authors' response
Bibliographic record
Abstract
1Exponent, Inc., Alexandria, VA, USA; 2Department of Pharmacotherapy, University of Utah, Salt Lake City, UT, USA; 3Aventis US Pharma, Bridgewater, NJ, USA; 4McMaster University School of Medicine, Hamilton, Ontario, Canada Sir, Thank you for the letter from Monnet et al.1 regarding our manuscript, ‘Meta-analysis of bacterial resistance to macrolides’.2These investigators raise a number of important points. First is the issue of local factors affecting bacterial resistance. Clearly, local factors can affect resistance, although we do not believe it has been documented, as Monnet et al. state, that levels of resistance ‘depend almost solely on local factors’. With the ever increasing rates of travel and global interactions, spread of resistance between different locales will probably continue to be an important issue. For example, a study by other researchers at Monnet's institute in Denmark reported that foreign travel was a risk factor for quinolone-resistant Campylobacter jejuni infections.3 A recent report also indicated that bacteria collected from international travellers to the same geographic region may not share the same DNA restriction patterns.4 Regardless, our meta-analysis would have been incomplete if we had not explored the potential heterogeneity of the included studies. All of the included studies involved similar criteria in assessing resistance: isolates of Streptococcus pneumoniae and/or Streptococcus pyogenes obtained from community or outpatient settings in 1997–2003 with specified MIC levels. Further, statistical analyses were performed (using the Q-statistic) to evaluate potential heterogeneity between studies. Thus, despite the potential effects of local factors, we were able to identify a statistically homogenous group of studies for pooling in the meta-analysis.
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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 | 0.087 | 0.480 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.015 | 0.013 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".