Evaluation of the Enhanced Invasive Pneumococcal Disease Surveillance System (eIPDSS) Pilot Project
Bibliographic record
Abstract
BACKGROUND: Invasive pneumococcal disease (IPD) causes significant morbidity in Canada, yet even with routine surveillance, it is difficult to interpret current IPD trends in serotype distribution and antimicrobial resistance. The enhanced Invasive Pneumococcal Disease Surveillance System (eIPDSS) pilot project was designed to facilitate a better understanding of IPD trends at the national level by linking epidemiologic and laboratory (epi-lab) data. OBJECTIVES: To evaluate the eIPDSS by assessing five attributes (usefulness, data quality, simplicity, acceptability and timeliness) and to develop recommendations for future national IPD surveillance. METHODS: An evaluation was developed that assessed the five key attributes through a qualitative survey sent to eight eIPDSS users as well as a quantitative analysis of the eIPDSS database. Recommendations were based on the results of both the survey and the analysis. RESULTS: The response rate to the survey was 100%. The majority of the survey respondents found the eIPDSS to be useful (75%), simple (100%) and acceptable (86%). Analysis of the eIPDSS database revealed that the majority of IPD cases (61%) were assessed as timely. Data quality and data management mechanisms were identified as issues by both survey respondents and the analysis of the database. Consultation with public health, regular audits and upgrades to the platform are recommended to address data quality and management issues. CONCLUSION: The epi-lab linked data of the eIPDSS enables the detection and analysis of IPD serotype distribution and antimicrobial resistance trends. This web-based system facilitates data collection and is simple, acceptable and timely. With improvements that address data quality and management issues, it is feasible to develop a national surveillance system that links epi-lab data.
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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 | 0.089 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".