The influence of viewing time on visual diagnostic accuracy: Less is more
Bibliographic record
Abstract
BACKGROUND: Understanding the factors that contribute to diagnostic errors is critical if we are to correct or prevent them. Some scholars influenced by the default interventionist dual-process theory of cognition (dual-process theory) emphasise a narrow focus on individual clinician's faulty reasoning as a significant contributor. In this paper, we examine the validity of claims that dual process theory is a key to error reduction. METHODS: We examined the relationship between a clinical experience (staff and resident physicians) and viewing time on accuracy for categorising chest X-rays (CXRs) and electrocardiograms (ECGs). In two studies, participants categorised images as normal or abnormal, presented at viewing times of 175, 250, 500 and 1000 ms, to encourage System 1 processing. Study 2 extended viewing times to 1, 5, 10 and 20 s to allow time for System 2 processing and a diagnosis. Descriptives and repeated measures analysis of variance were used to analyse the proportion of true and false positive rates (TP and FP) as well as correct diagnoses. RESULTS: In Study 1, physicians were able to detect abnormal CXRs (0.78) and ECGs (0.67) with relatively high accuracy. The effect of experience was found for ECGs only, as staff physicians (0.71, 95% CI = 0.66-0.75) had higher ECG TP than resident physicians (0.63, 95% CI = 0.58-0.68) in Study 1, and staff had lower ECG FP (0.10, 95% CI = 0.03-0.18) than resident physicians (0.27, 95% CI = 0.20-0.33) in Study 2. In other comparisons, experience was equivocal for ECG FPs and CXR TPs and FPs. In Study 2, overall diagnostic accuracy was similar for both ECGs and CXRs, (0.74). There were small interactions between experience and time for TP in ECGs and FP in CXRs, which are discussed further in the discussion and offer insights into the relationship between processing and experience. CONCLUSION: Overall, our findings raise concerns about the practical application of models that link processing type to diagnostic error, or to specific diagnostic error reduction strategies.
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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 | 0.009 | 0.110 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".