The neural correlates of medical expertise.
Bibliographic record
Abstract
Previous research using event-related potentials (ERPs) has shown that the N170 component is enhanced when experts categorize objects in their domain of expertise relative to when they categorize objects outside of their domain (Tanaka & Curran, 2001). Here, we replicated Tanaka and Curran’s study on bird and dog experts with medical experts in reading electrocardiography (ECG) and chest X-ray (CXR) images. 16 physicians (8 cardiologists, 8 pulmonologists) participated in the ERP study. On a scale of 1 (zero expertise) to 9 (much expertise), cardiologists and pulmonologists rated their expertise with ECG images as 8.50 and 5.75, respectively, and with CXR images as 5.00 and 7.63, respectively. On each of 520 trials, participants viewed one of 7 ECG or CXR patterns. The pattern image was preceded by a correct or incorrect label either at the basic (“ECG”, “CXR”) or subordinate (e.g., “attrial flutter”, “pneumonia”) level. Participants were asked to indicate with a key press whether the label and diagnostic image matched or not. A mixed ANOVA on the accuracy data from both groups showed a significant interaction between stimulus type and expertise—participants were better at categorizing ECG than CXR images, particularly the Cardiologists (F = 5.8, p < .05). Closely reflecting the behavioural results, a mixed ANOVA on the N170 mean amplitudes from correct trials showed a significant interaction between stimulus type, expertise, and hemisphere. The N170 was larger to ECG than CXR images, especially for Cardiologists, and in the right hemisphere (F = 6.97, p < .05). In contrast, no significant effects were found for the P100. These findings indicate that the N170 amplitude not only reflects object expertise but can also be modulated by expertise in pattern recognition. Meeting abstract presented at VSS 2015
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".