Bibliographic record
Abstract
Problem addressed Influenza-like illness (ILI) is a global and national concern. The surveillance of ILI requires collaborative efforts from many diverse settings, including primary care clinics. Objective of program To develop a sustainable reporting mechanism that enables primary care practices to provide ILI surveillance information to public health (PH) and addresses the needs of primary care practices and PH. Program description An automated, electronic ILI reporting program that collects information on ILI activity directly from family physicians; the program is integrated with the practice’s electronic medical record (EMR) system and therefore does not require physician initiation or disrupt physician workflow. Surveillance information is collected from a random sample of patient encounters using an automated pop-up screen that appears when exiting the patient’s EMR. Weekly summary reports are transmitted electronically to PH. Conclusion The EMR-integrated physician ILI reporting program is a simple and inexpensive way for family physicians to provide PH with important real-time, community-level disease surveillance information that is both complete and accurate. The program has been used in Hamilton, Ont, since 2004, which clearly demonstrates that it is a feasible and sustainable program in practice.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".