Run a PubMed search and judge every hit against the user's query / clinical question (CQ).

Search, abstract retrieval and judgement all happen server-side; only the decisions come back. Use a broad, sensitive query - screening is cheap (hundreds of articles in seconds) - and let the judgement do the narrowing.

Args: pubmed_query: PubMed search expression (field tags, MeSH, boolean operators). Put date, language and numeric limits here, e.g. ("2021/01/01"[dp] : "3000"[dp]); Jev is unreliable with numbers and dates. research_question: The user's query or CQ, as ONE self-contained sentence in ENGLISH (Jev is most accurate in English and reads literally - translate Japanese input, spell out abbreviations, avoid negations/double negatives). max_results: Screen at most this many hits, taken in PubMed relevance order (max 5000). Check "total_hits" against "screened" in the result to see whether hits were left out. inclusion_criteria: Optional extra criteria, each a short positive English statement (e.g. "The study is a randomized controlled trial"). An unmet criterion demotes include to maybe; it never excludes, since abstracts often omit such details. exclusion_criteria: Optional; a confidently met criterion excludes the article (e.g. "The article is a case report"). include_threshold: match probability at or above which an article is included. exclude_threshold: match probability at or below which an article is excluded. return_decisions: Which groups to list in "results". Default ["include", "maybe", "error"]; "counts" always covers every article. Add "exclude" only for small batches. save_full_results_to: Optional file path (.json). Every result, including excluded articles, is written there in full detail. Fails if the file already exists. detailed: False (default) returns one line per article, grouped by decision: "PMID | match | title". True returns every probability per article (much longer).

Judge whether the given PubMed articles match the user's query / clinical question (CQ).

Use this when you already have PMIDs; use search_and_screen to search and judge in one step. Titles and abstracts are fetched server-side from PubMed, so abstracts stay out of the conversation. Each article gets include / maybe / exclude plus the underlying probabilities, sorted by match probability.

Args: research_question: The user's query or CQ, as ONE self-contained sentence in ENGLISH (Jev is most accurate in English and reads literally - translate Japanese input, spell out abbreviations, avoid negations/double negatives). pmids: PubMed IDs to screen. inclusion_criteria: Optional extra criteria, each a short positive English statement (e.g. "The study is a randomized controlled trial"). An unmet criterion demotes include to maybe; it never excludes, since abstracts often omit such details. exclusion_criteria: Optional; a confidently met criterion excludes the article (e.g. "The article is a case report"). include_threshold: match probability at or above which an article is included. exclude_threshold: match probability at or below which an article is excluded. return_decisions: Which groups to list in "results". Default ["include", "maybe", "error"]; "counts" always covers every article. Add "exclude" only for small batches. save_full_results_to: Optional file path (.json). Every result, including excluded articles, is written there in full detail. Fails if the file already exists. detailed: False (default) returns one line per article, grouped by decision: "PMID | match | title". True returns every probability per article (much longer).

Same as screen_pmids, for articles that are not in PubMed (CiNii, arXiv, Embase exports...).

Args: research_question: The user's query or CQ as one self-contained English sentence. records: List of {"id": str, "title": str, "abstract": str}. English text works best. Other arguments: see screen_pmids.