Survey of feasibility of a peelable and point-of-use labelling system
Bibliographic record
Abstract
Objectives This article describes a collaborative survey undertaken along with clinical practitioners in the UK and in Canada, in the fall of 2008. Its purpose was to identify the risks associated with poor labelling of injectable medicines. Additionally, it seeks to make recommendations to improve patient safety through the use of an innovative labelling system, including ‘peelable labels with patient-specific data’ and ‘point-of-use drug information labels’. Methods In order to assess the use of these labelling systems, clinicians were surveyed regarding their opinions on ‘peelable labels with patient-specific data’ and ‘point-of-use drug information labels’. Results Practitioners expressed their support for the use of an innovative labelling system, including ‘peelable labels with patient-specific data’ and ‘point-of-use drug information labels’. Conclusions Due to the deleterious consequences of medication errors, health systems are always seeking ways to improve and prevent these errors. Innovative drug labelling is one mechanism that can be used to prevent errors. A ‘peelable label with patient-specific data’ can be affixed to a syringe. This is one proposed mechanism to ensure that patients receive the correct medication and the correct dose. A ‘point-of-use drug information label’ provides clinicians with drug information at the site of treatment, which may prevent mistakes.
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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 | 0.037 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".