Bibliographic record
Abstract
There are three domains of expertise required for consistently effective performance in emergency medicine (EM): procedural, affective, and cognitive. Most of the activity is performed in the cognitive domain. Studies in the cognitive sciences have focused on a number of common and predictable biases in the thinking process, many of which are relevant to the practice of EM. It is important to understand these biases and how they might influence clinical decision-making behavior. Among the specialities, EM provides a unique clinical milieu of inconstancy, uncertainty, variety, and complexity. Injury and illness are seen within narrow time windows, often under pressured ambient conditions. These operating characteristics force practitioners to adopt a distinctive blend of thinking strategies. Principal among them is the use of heuristics, a form of abbreviated thinking that often leads to successful outcomes but that occasionally may result in error. A number of opportunities exist to overcome interdisciplinary, linguistic, and other historical obstacles to develop a sound approach to understanding how we think in EM. This will lead to a better awareness of our cognitive processes, an improved capacity to teach effectively about cognitive strategies, and, ultimately, the minimization or avoidance of clinical error.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.016 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".