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Deep Sit: a system for studying and exam preparation

Bassiouni, A.; Shaw, C.-K. L.

2025-10-31 medical education
10.1101/2025.10.29.25338960 medRxiv
Show abstract

Studying for medical exams is a great undertaking traditionally considered a "rite of passage", as the depth and breadth of knowledge required increases progressively from undergraduate-, to postgraduate-, to fellowship-level exams. A common theme of these exams is the necessity of learning and committing to memory a large body of work, corroborating multiple disparate primary sources. The article presents new software, written to assist exam candidates with the process of knowledge acquisition and exam preparation. The software is written as an open-source plugin of the popular free note-taking software Obsidian and provides tight integration with the open-source reference management software Zotero. We then present a study system termed "Deep Sit" which incorporates a simple heading-based organization of medical knowledge: (Definition; Epidemiology; Etiology; Pathophysiology; Symptoms & Signs; Investigations; Treatment). We demonstrate how the Deep Sit study system is significantly facilitated through a walkthrough of these software components.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

1
PLOS ONE
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Top 9%
19.1%
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5
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5.7%
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7
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Top 0.4%
3.3%
8
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◐ 18 papers in training set
Top 0.1%
3.3%
9
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○ 24 papers in training set
Top 0.4%
2.5%
10
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○ 152 papers in training set
Top 3%
1.7%
11
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◐ 42 papers in training set
Top 1%
1.5%
12
Frontiers in Medicine
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1.5%
13
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1.5%
14
Journal of Clinical Medicine
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Top 3%
1.4%
15
International Journal of Medical Informatics
○ 26 papers in training set
Top 0.8%
1.4%
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1.4%
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Top 1%
1.4%
19
eLife
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Top 57%
1.2%
20
Frontiers in Public Health
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Top 5%
1.0%
21
Journal of Visualized Experiments
● 34 papers in training set
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1.0%
22
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◐ 14 papers in training set
Top 0.4%
0.9%
23
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Top 1%
0.6%
24
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Top 6%
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25
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26
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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.