Symptom Experience and Influenza-Like Illness in a Military Population
Bibliographic record
Abstract
AIMS: The primary objective of this study was to identify if symptom presentation expressed over the course of an influenza-like illness (ILI) can predict virus type by use of unsupervised machine learning. The secondary objective was to describe clinical characteristics of strain specific coronavirus. Finally, examine the psychometric properties of the Canadian Acute Respiratory Illness and Flu Scale (CARIFS). BACKGROUND: ILI outbreaks have been a significant source of non-battle injury among military personnel. Many different viruses cause ILI, and it is difficult to determine which virus is causing the illness. Recent studies have examined the etiology and epidemiology of ILIs. Other studies have examined influenza virus symptom severity either a dichotomous or liner-sum analysis. No studies to the researcher’s knowledge have examined ILI symptoms through an unsupervised learning analysis, and few studies have examined self-reported outpatient ILI reported symptoms over an extended time frame. METHODS: This is a secondary analysis of data collected over a four year period by the Acute Respiratory Infection Consortium (ARIC), from an otherwise healthy military population. The symptom data was captured on visit days and by a symptom diary patients filled out at home using a symptom severity instrument designed for this study. FINDINGS: Clustering by unsupervised machine learning was unable to predict virus type based on physical symptom presentation over the course of ILI. It did identify patient attributes, like sex and age that caused patients to experience symptoms differently. Additionally, clinical similarities and differences were noted between the four common human coronavirus strains. The strain HKU1 tended to have higher systemic symptom scores and higher gastrointestinal symptom severity score over the course of illness when compared to the other strains. Finally, the psychometric properties of CARIFS revealed many strengths and limitations for its use in research. The CARIFS should be reexamined using current knowledge of symptom management to increase the validity of the instrument. IMPLICATIONS: The results demonstrated how individuals experience physical symptoms differently making it difficult to predict the viral strain causing ILI. Future research should focus on the development of symptom instruments using the theoretical underpinnings of the symptom management theory.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".