Artificial intelligence to improve antimicrobial prescribing: A protocol for a systematic review
Bibliographic record
Abstract
<ns3:p> <ns3:bold>Introduction:</ns3:bold> The inappropriate use of antimicrobials is a threat to their effectiveness and often results in antimicrobial resistance (AMR) and difficult to treat infections. Different methods have been implemented to control AMR, and in recent years, artificial intelligence (AI) has been used to improve antimicrobial prescribing. However, there is insufficient information about the contribution of AI in improving antimicrobial prescribing. This systematic review aims to determine whether the use of AI can improve antimicrobial prescribing for human patients. </ns3:p> <ns3:p> <ns3:bold>Methods:</ns3:bold> Observational studies that examine the potential or actual use of AI in improving antimicrobial prescribing cited in IEEE Xplore, ScienceDirect, Scopus, Web of Science, OVID, EMBASE and ACM will be included in this systematic review. There will be no restriction on language, nor the setting (i.e.: primary care or hospital) nor the time when the studies included were conducted. The primary outcome of this systematic review is the relative reduction in prescribed antimicrobials, while the secondary outcome is the relative reduction in patients’ consultations, whether for infection recurrence or worsening of symptoms. Data will be meta-analyzed with a Random Effects Model. The I <ns3:sup>2</ns3:sup> statistic for heterogeneity will be calculated and the Newcastle Ottawa Scale Tool will be used to assess risk of bias. </ns3:p> <ns3:p> <ns3:bold>Dissemination:</ns3:bold> The results will be disseminated through a peer-reviewed publication and scientific sessions. </ns3:p> <ns3:p> <ns3:bold>PROSPERO Registration:</ns3:bold> This protocol has been registered in PROSPERO online database (CRD42022329049; 14 May 2022). </ns3:p>
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How this classification was reachedexpand
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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".