Development of medical checklists for improved quality of patient care
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Checklists are used in both medical and non-medical industries as cognitive aids to guide users through accurate task completion. Their development requires a systematic and comprehensive approach, particularly when implemented in high intensity fields such as medicine. OBJECTIVE: A narrative review of the literature was conducted to outline the methodology to designing and implementing clear and effective medical checklists. METHODS: We systematically searched for relevant English-language medical and non-medical literature both to describe where checklists have been demonstrated to improve delivery of care and also, how to develop valid checklists. RESULTS: The MEDLINE search yielded 8303 citations of which 1042 abstracts were reviewed. On the basis of criteria for inclusion and subsequent full-manuscript review, 178 sources, including 17 non-medical publications, were included in the narrative review. This information was further supplemented by expert opinion in the area of checklist development and implementation. A small number of strategies for designing effective checklists were referenced in the literature, including utilization of pre-published guidelines, formation of expert panels and repeat pilot-testing of preliminary checklists. CONCLUSION: Despite currently available evidence, a highly effective, standardized methodology for the development and design of medical-specific checklists has not previously been developed and validated, which has likely contributed to their inconsistent use in several key fields of medicine, despite evidence of their fundamental role in error management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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 it