Skill UI

This skill provides a comprehensive guide for authors assessing the fit and framing of metabolic, endocrine, or diabetes research for The Lancet Diabetes & Endocrinology. It outlines the journal's specific requirements, favoring major randomized trials, large prospective cohorts, and population-level epidemiological studies with clear international clinical or policy relevance. It covers method standards, reporting guidelines (CONSORT, STROBE, PRISMA), and official submission checklists. Note: This is a venue-fit aid, not clinical or regulatory advice.

A comprehensive guide for researchers on assessing the suitability of digital health, AI/ML, or health informatics manuscripts for The Lancet Digital Health. It outlines the journal's strict standards for clinical rigor, external validation, reporting guidelines (TRIPOD, CONSORT-AI), and evidence required for publication.

This skill provides comprehensive guidelines for submitting clinical neurology and neuroscience research to The Lancet Neurology. It assesses the journal's scope (e.g., Phase III trials, global burden), the necessary evidence bar (CONSORT, STROBE, PRISMA compliance), and the required structure (structured abstract, 'Added value' panel). Use this tool to re-frame and optimize your manuscript to meet high-impact academic publication standards.

This comprehensive skill acts as a venue-selection and re-framing tool for authors targeting The Lancet Oncology. It provides deep insight into the journal's specific requirements, including methodological rigor (CONSORT, PRISMA), required evidence strength (Phase III RCTs, definitive biomarkers), structural elements (structured abstract, 'Added value' panel), and current submission best practices. Use this guide to assess if your clinical oncology manuscript is sufficiently advanced and impactful for this prestigious global journal.

This skill provides comprehensive guidance for submitting high-impact clinical and population research to The Lancet Respiratory Medicine. It details the journal's specialized scope, which favors practice-changing randomized trials and major prospective cohorts in respiratory and critical care. The guide highlights the rigorous evidence bar, including expected reporting guidelines (CONSORT, STROBE, PRISMA), statistical rigor, and structural house style, serving as a crucial fit and framing aid for authors.

This skill transforms raw genomic identifiers (VCF, HGVS, rsID) into highly annotated, consequence-predicted records. It utilizes powerful, open-source tools like Ensembl VEP and gnomAD to predict variant consequences (e.g., missense, nonsense), map coordinates, and attach population allele frequencies. Furthermore, it links these molecular findings to clinical and phenotype context extracted by OpenMed, forming a comprehensive research record.

This utility packages multiple independent FHIR R4 resources into a single, valid Transaction Bundle. It automatically handles deterministic URN UUID generation and resolves internal cross-references, making the bundle immediately ready for POSTing to a FHIR server. This is crucial for maintaining data integrity and interoperability when submitting multiple related clinical resources.

This skill performs an adversarial second pass to scan already de-identified clinical text for residual identifiers (PHI/PII). It combines deterministic regex checks (SSN, phone, dates) with a model-based PII detection rerun. It scores severity and implements a hard block rule: any detected leak prevents data release, ensuring the highest standard of data privacy and compliance.

Designed for regulated environments (GxP/GCP), this skill generates and verifies audit trails compliant with 21 CFR Part 11 standards. It ensures every data processing step leaves an attributable, time-stamped, and tamper-evident record, providing electronic signatures and robust data integrity checks (ALCOA+). It is essential for clinical trials and regulated data governance.

This skill automates the creation of comprehensive Model Cards for OpenMed clinical NER or de-identification models. It gathers data from various OpenMed evaluation reports (e.g., `GateReport`, `fairness_report`, `error_report`) to structure critical governance information, including intended use, quantitative performance metrics, subgroup analysis, limitations, and required medical-device disclaimers. It ensures documentation is reproducible and compliant for clinical AI deployment.

A robust, on-device solution for processing large-scale clinical text corpora. Supports batch NER, PII extraction, and de-identification over thousands of notes. Features include sharding, checkpointing, resumable functionality, and streaming results to JSONL, ensuring PHI safety and constant memory usage for massive datasets.

This skill provides an in-depth, entity-level scorecard for clinical or biomedical Named Entity Recognition (NER) models. It calculates precision, recall, and F1 scores (both strict and partial match modes) using a user-supplied gold corpus. Key features include per-label error breakdown, a confusion matrix, and detailed False Negative/False Positive analysis, essential for debugging model performance in highly sensitive medical domains.