Use of a structured panel process to define antimicrobial prescribing appropriateness in critical care
Bibliographic record
Abstract
BACKGROUND: Antimicrobial prescribing is frequently reported as appropriate or inappropriate, particularly in the ICU. However, the definitions used are non-standardized and lack validity and reliability. OBJECTIVES: To develop standardized definitions of appropriateness for antimicrobial prescribing in the critical care setting. METHODS: We used consensus-based modified Delphi and RAND appropriateness methodology to develop criteria to define appropriateness of antimicrobial prescribing. A multiphased approach with an online questionnaire followed by a facilitated in-person meeting was utilized and included clinicians from a variety of practice areas (e.g. surgeons, infectious diseases specialists, intensivists, transplant specialists and pharmacists). RESULTS: There were a total of 23 criteria agreed upon to define the following categories of antimicrobial prescribing: appropriate; effective but unnecessary; inappropriate; and under-treatment. CONCLUSIONS: These standardized criteria for appropriateness may be generalizable to other patient populations and utilized with other tools to adjudicate prescribing practices.
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 | 0.152 | 0.180 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".