Validation of an Infarction Code Care Checklist and Determination of its Relationship With Other Patient Safety Indicators: Protocol for a Prospective Study
Bibliographic record
Abstract
BACKGROUND: In the care of time-dependent illnesses, facilitating care and systematizing actions with a checklist provides security to health professionals and reduces errors, thereby increasing patient safety. However, despite the widespread use of checklists in other clinical contexts, no studies have yet validated a checklist specifically for infarction code care. OBJECTIVE: The objective of this study is to validate the checklist and determine its relationship with the rest of the patient safety indicators in the primary care teams of the Catalan Health Institute of Central Catalonia. METHODS: This is a prospective study for the validation of a checklist for infarction code care. In this study, 2 clinical scenarios of varying difficulty are defined, and the correct answers are established in each case according to the gold standard guidelines. During the first 3 months of the ongoing year, we held an annual training meeting where infarction code referents from various primary care teams gathered to review the new guidelines and outline the training strategy for the next year. These referents conducted annual training sessions for their respective teams before Easter, during which they explained the new guidelines. On the same day as the training, the 2 clinical scenarios were completed using the online version of the checklist for the first time for all participants. The checklist was sent in digital format to all health professionals who responded the first time, and then a reminder was sent to respond a second time at 30, 45, and 90 days to obtain the maximum number of second responses, as the checklist should be completed twice to assess internal reliability and temporal robustness. The number of hits was compared with respect to the gold standard for both the first and the second response. The results obtained from the responses and accuracies, when compared with the gold standards, were evaluated against other available patient safety indicators in the region. RESULTS: Between January 2023 and May 2023, we obtained 615 responses to the online version of the checklist. We conducted analyses to assess both internal consistency and temporal robustness of the responses and have also structured the framework for comparing these results with other patient safety indicators available in the region. Data analysis is currently underway, and we expect to publish the results in early 2026. CONCLUSIONS: If the checklist demonstrates strong internal consistency and temporal robustness and shows a meaningful relationship with patient safety indicators, it could be implemented across primary care centers using the infarction code. This would support safer, more standardized care in time-sensitive clinical situations. TRIAL REGISTRATION: IDIAP Jordi Gol 4R22/343; https://idiapjgol.org/grup-recerca/prosaaru/projectes/. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/66584.
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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 | 0.065 | 0.064 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.034 | 0.011 |
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".