Effectiveness, barriers and facilitating factors of strategies for active delabelling of patients with penicillin allergy labels: a systematic review protocol
Bibliographic record
Abstract
INTRODUCTION: Up to 15% of adult patients in the clinical setting report to be allergic to penicillin. However, in most cases, penicillin allergy is not confirmed. Due to the negative aspects associated with erroneous penicillin allergy, the implementation of active delabelling processes for penicillin allergy is an important part of antibiotic stewardship programmes. Depending on the clinical setting, different factors need to be considered during implementation. This review examines the effectiveness of different delabelling interventions and summarises components and structures that facilitate, support or constrain structured penicillin allergy delabelling. METHODS AND ANALYSIS: This review will adhere to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. The databases MEDLINE (via PubMed), EMBASE and Cochrane Library were searched for studies reporting on any intervention to identify, assess or rule out uncertain penicillin allergy. To improve completeness, two further databases are also searched for grey literature. Study design, intervention type, professional groups involved, effectiveness, limitations, barriers, facilitating factors, clinical setting and associated regulatory factors will be extracted and analysed. In addition, exclusion criteria for participation in the delabelling intervention and criteria for not delabelling penicillin allergy will be summarised. In case of failed protocols, these are highlighted and quantitatively analysed if possible. Two independent reviewers will perform the screening process and data extraction. Discordant decisions will be resolved through review by a third reviewer. Bias assessment of the individual studies will be performed using the Newcastle Ottawa Scale. ETHICS AND DISSEMINATION: Because individual patient-related data are not analysed, an ethical approval is not required. The review will be published in a peer-reviewed scientific journal.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 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".