PHARMACISTS ’ ROLE IN PREVENTING MEDICATION ERRORS WITH POTASSIUM
Bibliographic record
Abstract
Arecently announced coroner’s inquest will reviewthe case of Frances Marie Tanner, who died in an Ontario hospital on January 21, 2002, as a result of accidental injection of concentrated potassium chloride. As health care professionals, we are spurred to action to take steps to prevent future tragedies of this nature. On the basis of previous documentation of cases of error-induced injury with potassium chloride and the resultant recommendations,1-6 we can anticipate the system remedies that will be highlighted in the upcoming inquest. There is thus no need to await the outcome of the inquest — we can start to implement more safeguards in our medication-use systems today, to prevent mishaps with potassium chloride. Successful implementation of system changes to prevent error-induced injury with potassium chloride
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".