Using real-time alerts for clinical trials
Bibliographic record
Abstract
BACKGROUND: Clinical trials are widely accepted as a necessary step in evaluating the safety and efficacy of new pharmaceutical products. In order for a sufficiently powered study, a clinical trial depends on the effective and unbiased recruitment of eligible patients. Trials involving seasonal diseases like influenza pose additional challenges. OBJECTIVE: This is a feasibility study of a mobile real-time alerting system to systematically identify potential study subjects for a randomized controlled trial evaluating the safety and efficacy of early intervention with interferon alfacon-1 for patients hospitalized for influenza virus infection. METHODS: The alerting system was setup in a 471-bed acute care teaching hospital, enabled with computerized physician order entry (CPOE) and a rules-based alerting system. Patients were identified from the entire hospital using two alerts types: pharmacy prescription records for antiviral drugs, and positive influenza laboratory results. Email alerts were generated and sent to BlackBerry(®) devices carried by the study personnel for a 6 month period. The alerts were archived automatically on a secure server and were exported for analysis in Microsoft Access. RESULTS: Over a period of 21 weeks, 779 total alerts were received. The study team was alerted to 241 patients, of whom 85 were potential study subjects. The alert system identified all but one of the patients independently identified by infection control. CONCLUSIONS: Real-time identification of potential study subjects is possible with the integration of computerized physician order entry and BlackBerry(®) technology. It is a viable method for the systematic identification of patients throughout a hospital, particularly for trials investigating time-sensitive disease progression.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.220 | 0.428 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".