Design characteristics that affect speed of information access and clarity of presentation in an electronic neuroanatomy atlas
Bibliographic record
Abstract
Functional Neuroanatomy, an interactive electronic neuroanatomical atlas, was designed for first year medical students. Medical students have much to learn in a limited time; therefore a major goal in the atlas design was that it facilitate rapid, accurate information retrieval. To assess this feature, we designed a testing scenario in which students who had never taken a neuroanatomy course were asked to complete two equivalent tests, one using the electronic atlas and one using a comparable hard copy atlas, in a limited period of time. The tests were too long to be completed in the time allotted, so test scores were measures of how quickly correct information could be retrieved from each source. Statistical analysis of the data showed that the tests were of equal difficulty and that accurate information retrieval was significantly faster using the electronic atlas when compared with the hard copy atlas (P < 0.0001). Post-test focus groups (n = 4) allowed us to infer that the following design features contributed to rapid information access: the number of structures in the database was limited to those that are relevant to a practicing physician; all of the program modules were presented in both text and image form on the index screen, which doubled as a site map; pages were layered electronically such that information was hidden until requested, structures available on each page were listed alphabetically and could be accessed by clicking on their name; and an illustrated glossary was provided and equipped with a search engine.
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 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 | 0.013 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".