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.
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 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 | 0.001 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".