Focusing on antimicrobial resistant infections –are we missing the forest for the trees and the patients for pathogens?
Bibliographic record
Abstract
Antimicrobial resistance (AMR) is a challenge because it is associated with worse patient outcomes. To solve the problem will take development of interventions and policies which improve patient outcomes by prolonging survival, improving patient symptoms, function and quality of life. Logically, we should look to focusing resources in areas that would have the greatest impact on public health. AMR takes the approach of focusing on individual pathogens and "pathogen-focused" development. However, evaluating overall infections and their impact on patient outcomes reveals that 17 of 18 infection deaths are associated with susceptible pathogens. Here we discuss recentering on patients and patient outcomes instead of pathogens, and propose six suggestions on how a patient focus impacts areas and incentives for clinical research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.036 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".