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The Value of Basic Science in Clinical Diagnosis

2006· article· en· W2041600754 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAcademic Medicine · 2006
Typearticle
Languageen
FieldMedicine
TopicClinical Reasoning and Diagnostic Skills
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRecallTest (biology)EpidemiologyClinical epidemiologySet (abstract data type)Basic scienceValue (mathematics)DiseaseMedicineMedical scienceMedical educationPsychologyCognitive psychologyComputer sciencePathology

Abstract

fetched live from OpenAlex

BACKGROUND: The role of basic science knowledge in clinical diagnosis is unclear. There has been no experimental demonstration of its value in helping students recall and organize clinical information. This study examines how causal knowledge may lead to better recall and diagnostic skill over time. METHOD: Undergraduate medical students learned either four neurological or rheumatic disorders. One group learned a basic science explanation for the symptoms. The other learned epidemiological information. Both were then tested with the same set of clinical cases immediately after learning and one week later. RESULTS: On immediate test, there was no difference in accuracy (70% for both groups). However, one week later, performance in the epidemiology group dropped to 51%; the basic science group only dropped to 62%. CONCLUSIONS: Basic science knowledge relating causal knowledge to disease symptoms can improve diagnostic accuracy after a delay.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.156
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score0.962

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.156
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.046
GPT teacher head0.421
Teacher spread0.374 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it