SODAPOP: A Metacognitive Mnemonic Framework to Teach Antimicrobial Selection
Bibliographic record
Abstract
Mnemonics are used widely throughout medical education to help manage large amounts of information and to promote a systematic approach to complex problems. SODAPOP is a metacognitive mnemonic that offers learners a framework for veterinary clinical decision making to support optimal antimicrobial selection. SODAPOP has students consider the source and organism before they decide to treat; then they consider the antimicrobials to which the organism is susceptible with regard to contraindications in the patient; and, ultimately, the options are weighed and a plan is formulated. A preliminary study showed that students’ perception of SODAPOP was favorable and that exposure to SODAPOP improved student confidence levels. Further research is needed to determine whether SODAPOP improves students’ optimal antimicrobial selection. SODAPOP could be a potentially helpful teaching tool because it can be mapped to the Association of American Veterinary Medical Colleges competency-based veterinary education framework under subcompetencies 1.3 and 4.2. A mnemonic such as SODAPOP could be integrated throughout the veterinary curriculum both in basic science courses (microbiology) and with real cases during clinical rotations.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".