Developing and implementing techniques to harvest surveillance information from existing veterinary diagnostic laboratory data
Bibliographic record
Abstract
Syndromic surveillance is a tool for continuous, automated extraction of surveillance information\nfrom health data sources. The research documented in this dissertation aimed at exploring informatics\nand data mining tools in order to develop and implement techniques to harvest additional\nsurveillance information from existing diagnostic laboratory data. Data concerning laboratory test\nrequests for diagnosis in cattle were provided by the Animal Health Laboratory (AHL), at the University\nof Guelph, Ontario. A thorough review of the initiatives of syndromic surveillance in animal health was conducted. Documented difficulties\nregarding the acquisition of clinical data, and especially sustainability of systems based on voluntary participation of veterinarians or data providers\nin scattered locations, resulted in the choice of using laboratory data in this research. Automated\nmethods to classify laboratory submission data into clinical syndromes were investigated. One of\nthe challenges of working with laboratory data was determining how to transform diagnostic data\ninto epidemiological information. The most time-consuming step of class between these words, their\nco-occurrences and the target syndromic group. Once deAfter classidata for the algorithms implemented in the next stages. Lastly, the prospective phases of system\ndevelopment were carried out, that is, the analyses which scan the time series in an on-line process,\none day at a time, in order to detect temporal aberrations in comparison to a baseline of historical\ndata. Several aberration detection algorithms were evaluated. Upon the conclusion that no single\nalgorithm was superior in all outbreak scenarios, a scoring system to combine algorithms was developed.\nAll steps were set up using open source software, and delivered to the data provider as\na simple desktop application scheduled to run daily in an automated manner. Fast development\nand simple maintenance is expected to lead to incorporation of this system into the routine of\nthe data, becoming an indispensable tool for diagnosticians and epidemiologists, and encouraging\nfurther technical development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".