Exploring the Differences in Macrocognition Between Experts and Non-CHD Experts Managing Congenital Heart Disease (CHD)
Bibliographic record
Abstract
Background and aims: Children with congenital heart disease (CHD) are at risk of deterioration in the face of common childhood illnesses, and their resuscitation and acute management is often best achieved with the guidance of CHD experts. Access to such expertise may be limited outside specialty heart centers and the fragility of these patients is cause for discomfort among many emergency medicine physicians. An understanding of the differences in macrocognition of these clinicians could shed light on some of the causes of discomfort and facilitate the development of a sociotechnological solution to this problem. Methods: Cardiac intensivists (CHD experts) and pediatric emergency medicine physicians (non-CHD experts) in a major academic cardiac center were interviewed using the critical decision method. Interview transcripts were coded deductively based on Klein’s macrocognitive framework and inductively to allow for new or modified characterization of dimensions. Results: While both CHD-experts and non-CHD experts relied on the macrocognitive functions of sensemaking, naturalistic decision making and detecting problems, the specific data and mental models used to understand the patients and course of therapy differed between CHD-experts and non-CHD experts. Conclusion: Characterization of differences between the macrocognitive processes of CHD experts and non-CHD experts can inform development of sociotechnological solutions to augment decision making pertaining to the acute management of pediatric CHD patients.
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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 | 0.000 | 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".