The Ligurian Human Immunodeficiency Virus Clinical Network: A Web Tool to Manage Patients With Human Immunodeficiency Virus in Primary Care and Multicenter Clinical Trials
Bibliographic record
Abstract
BACKGROUND: In recent years, Highly-Active Anti-Retroviral Therapies (HAARTs) have modified the Human Immunodeficiency Virus (HIV) life-cycle and the disease is now considered chronic. Consequently, a longitudinal and complex follow-up is now required for HIV positive patients during their lifetime. Moreover, patients often encounter various complications due to comorbidities, related to the immunodeficiency state and HAARTs' side effects. Thus, HIV positive patients are involved in multicenter clinical trials (MCTs) to improve treatments and discover a preventive vaccine. Therefore, physicians require proper instruments to access comprehensive patient data for managing patients during follow-ups, and tools for data collection and analysis in MCTs. OBJECTIVE: The Ligurian HIV Clinical Network aims to provide physicians with a Web-tool to administrate HIV positive patients' data within primary-care and to reuse the collected clinical information to perform MCTs in Northern Italy. METHODS: The key aspect of the system is a relational database which allows the storage of various types of clinical information (eg, related to HIV, cardiovascular, or hepatic diseases) in multiple formats. The modular design of the database permits a rapid insertion of new parameters without requiring any changes in the database structure. Furthermore, codes from biomedical ontologies controlled vocabularies ("Logical Observation Identifier Names and Codes", and "International Classification of Diseases 9") and ontologies ("Systematized Nomenclature of Medicine Clinical Terms"), units and normality ranges used by all partners participating in the project were collected to achieve a complete semantic interoperability. Accordingly, data can be automatically normalized through the z score formula and physicians can extract and correctly compare information with external statistical tools. Moreover, to respect patients' privacy and legal issues, a local identifier, determined through an HASH cryptography algorithm, is assigned to each patient during the registration process. The database is managed by a user-friendly Web-platform which allows quick access to information during medical examinations and the reusing of the collected data for present and future MCTs. Furthermore, a bidirectional middleware was created in order to import/export information through HL7 messaging. Hence, data can be manually entered by physicians or automatically collected within HL7-compliant Hospital Information systems. RESULTS: Presently, the direct storage of patients' information from the San Paolo Hospital (Savona, Italy), and San Martino and Galliera hospitals in Genoa is in a test phase. Currently, 8 centers of Infectious Diseases (located in Liguria and Piedmont) are participating in the project and almost 400 HIV positive patients have been recorded in the system. Patient data has been used for primary care and research purposes. Currently, there are 4 on-going MCTs and preliminary results have already been presented at International HIV congresses. CONCLUSIONS: The Web-platform allows effective management, sharing and reuse of information within primary care and clinical research. In the future it is planned to share the clinical information from this network with other HL7-compliant workgroups and to extend the platform to other infective diseases (eg, hepatitis).
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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 | 0.023 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.005 |
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".