34047007@qq.com
a6cd99a4ca
OncoLit: a multi-tenant oncology literature search, feed, and collaboration platform. Built with FastAPI + Vue 3 + PostgreSQL. Includes PubMed pipeline, drug approvals, AI summaries, and systematic review tools.
53 lines
2.1 KiB
Python
53 lines
2.1 KiB
Python
import asyncio
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from sqlalchemy import text
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from sqlalchemy.ext.asyncio import create_async_engine
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DATABASE_URL = 'postgresql+asyncpg://scilit:scilit_prod_2026@postgres:5432/scilit'
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async def main():
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engine = create_async_engine(DATABASE_URL)
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async with engine.connect() as conn:
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# Check cited_by_count distribution
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r = await conn.execute(text("""
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SELECT
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CASE
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WHEN cited_by_count IS NULL OR cited_by_count = 0 THEN '0 or NULL'
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WHEN cited_by_count BETWEEN 1 AND 5 THEN '1-5'
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WHEN cited_by_count BETWEEN 6 AND 20 THEN '6-20'
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WHEN cited_by_count BETWEEN 21 AND 100 THEN '21-100'
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ELSE '100+'
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END as bucket,
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COUNT(*) as cnt,
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MAX(cited_by_count) as max_in_bucket
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FROM global_literature
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GROUP BY bucket
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ORDER BY MIN(COALESCE(cited_by_count, 0))
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"""))
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print('=== Cited by count distribution ===')
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for row in r:
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print(f' {row.bucket}: {row.cnt}')
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# Latest citation update time
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r = await conn.execute(text("""
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SELECT MAX(updated_at) as last_update, COUNT(*) as total_with_citations
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FROM global_literature WHERE cited_by_count IS NOT NULL AND cited_by_count > 0
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"""))
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for row in r:
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print(f'\nLast citation update: {row.last_update}')
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print(f'Articles with citations: {row.total_with_citations}')
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# Check what refresh_hot_articles_cache filters by
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r = await conn.execute(text("""
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SELECT cited_by_count, pub_date, pmid FROM global_literature
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WHERE cited_by_count IS NOT NULL AND cited_by_count > 0
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AND pub_date >= CURRENT_DATE - INTERVAL '365 days'
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ORDER BY cited_by_count DESC LIMIT 10
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"""))
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print('\nTop cited articles in last year:')
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for row in r:
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print(f' PMID={row.pmid} cited={row.cited_by_count} pub_date={row.pub_date}')
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await engine.dispose()
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asyncio.run(main())
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