Application of the Plan-Do-Check-Act Cycle for Managing Immune-Related Adverse Events
Bibliographic record
Abstract
Background: Immune checkpoint inhibitors (ICIs) sometimes cause immune-related adverse events (irAEs), the timing of occurrence of which is difficult to predict. We created a system to safely manage the patients treated with ICIs who visit hospital during an emergency. Methods: We utilized the Plan-Do-Check-Act (PDCA) cycle method to improve the quality of countermeasures for irAEs in the emergency room. First, an icon showing the patients treated with ICIs was developed for inclusion in electronic medical records. Second, ICI-specified urgent sets of clinical laboratory tests were prepared to cover the spectrum of irAEs. Third, a direct call system to either the attending physician or the chemotherapy team was established. A flow chart for managing irAEs has been prepared since September 2018. We retrospectively analyzed the electronic medical records from September 2018 to December 2020 to determine the effectiveness of the developed system. Results: In the first cycle of PDCA, 24 patients administered ICIs were retrospectively surveyed and seven visited the emergency room. Six cases were examined according to the flow chart, whereas the other patient complaining of grade 2 diarrhea were not examined because of incomplete knowledge regarding ICIs and irAEs. As part of the “Act” step, we reminded the doctors of the flow chart and gave a lecture to the residents on how to manage irAEs. During the second and seventh cycle, no cases were observed without consulting the flow chart. Conclusions: Quality improvement activities for the management of irAEs were conducted using the PDCA cycle methodology. Patients on ICIs are now being continuously monitored to further improve management quality.
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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 | 0.000 |
| 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".