Found in translation: the impact of familiar symptom descriptions on diagnosis in novices
Bibliographic record
Abstract
CONTEXT: The language that patients use to communicate with doctors is quite different from the language of diagnosis. Patients may describe tiredness and swelling; doctors, fatigue and oedema. This paper addresses the process by which novices, who have learned standard medical terms for symptoms, use lay descriptions of symptoms to reach a diagnosis. Data in this paper indicate that the familiarity of the language used to describe symptoms influences diagnosis in novices and diagnosis does not, therefore, involve a simple translation into standard terms that are the basis of diagnostic decision. METHODS: A total of 24 undergraduate students were trained to diagnose 4 pseudo-psychiatric disorders presented in written vignettes. Participants were tested on cases that contained 2 equally probable diagnoses, in 1 of which the symptoms were expressed using previously seen descriptions. A deviation from 50:50 in reported diagnostic probabilities was expected if the familiar symptom descriptions biased diagnostic decisions. Twelve participants were tested immediately after training and 12 after a 24-hour delay. RESULTS: Participants assigned greater diagnostic probability to the diagnosis supported by the familiar feature descriptions (F[1.242] = 19.35, P < 0.001, effect size = 0.40) on both immediate (52% versus 41%) and delayed (51% versus 38%) testing. DISCUSSION: The findings indicate that diagnosis is not simply based on a process of translating patient descriptions of symptoms to standard medical labels for those symptoms, which are then used to make a diagnosis. Familiarity of symptom description has an effect on diagnosis and therefore has implications for medical education, and for electronic decision support systems.
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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 | 0.011 | 0.147 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".