A Standardized, Structured Approach to Identifying Drug-Related Problems in the Intensive Care Unit: FASTHUG-MAIDENS
Bibliographic record
Abstract
Pharmacy practice in the intensive care unit (ICU) is complex, because of the high acuity of patients’ conditions and the large number of medications prescribed. Therefore, many pharmacists, especially those not familiar with ICU care, may feel overwhelmed and apprehensive in this setting. There is currently no standardized, structured approach to help pharmacists provide pharmaceutical care in the ICU. For that reason, routine tasks such as identifying drug-related problems are much harder to perform, and drug therapy is not always optimized. In 2005, the FASTHUG mnemonic was proposed as a standardized approach to help ICU physicians ensure that all essential aspects of care for critically ill patients are met. Implementation of the FASTHUG approach in a surgical ICU was subsequently shown to decrease the rates of ventilator-associated pneumonia. Although the mnemonic has been generally well received, some clinicians have modified it to better augment their particular ICU practices. Notably, the FASTHUG mnemonic was not designed to identify drug-related problems commonly seen in the ICU. Therefore, we developed a modified mnemonic, FASTHUG-MAIDENS, as a standardized, structured approach to identifying drug-related problems in the ICU (Table 1).
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".