Bibliographic record
Abstract
On behalf of my co-authors, I thank Dr. Webster for his observations and suggestions about our paper on the impact of critical event checklists in simulated operating room emergencies in an ambulatory setting 1. As research in this field emerges, I agree that our work highlights what we have yet to learn, as well as what we are incrementally learning. The notion of memory priming was not one we had considered as an alternative explanation for our observed data. In this case, we suspect that the precise nature of the orientation and familiarisation phase would not have been adequate to induce memory priming. The participants were given an overview of the cognitive aid (by slide presentation), then a opportunity to handle the binder and browse the checklists – sufficient to absorb the format and layout of the checklists but not enough time to digest each list in a point-by-point fashion. We wondered whether independent preparation and hyper-vigilance on the first encounter was replaced by a more relaxed attitude on the second encounter when performance without the checklists was then demonstrably inferior. We agree strongly that understanding how checklists can be incorporated into the culture of clinical practice is fundamental to their successful implementation and positive impact, because in circumstances where they are considered optional prompts, they fail to achieve their maximal benefit. It is encouraging that there are research groups worldwide looking to answer these questions.
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.
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 | 0.007 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.055 | 0.072 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".