Development of an Ordinal Scale Treatment Endpoint for Adults Hospitalized With Influenza
Bibliographic record
Abstract
BACKGROUND: An obstacle in influenza therapeutics development is the lack of clinical endpoints, especially in hospitalized patients. A single time-point binary outcome measure is limited by patients' diverse clinical trajectories and low event rates. METHODS: A 6-point ordinal scale with ascending clinical status severity (scoring: discharged; subacute care; acute care without/with respiratory failure; intensive care unit [ICU]; death) was proposed to study outcomes of adults hospitalized with influenza. Individual patient data from 2 active surveillance cohorts' datasets (2015/2016-2017/2018; Edmonton, Hong Kong) was used for evaluation. The impact of neuraminidase inhibitor (NAI) treatment on longitudinal ordinal outcome changes over 30 days was analyzed using mixed-effects ordinal logistic regression and group-based trajectory models. RESULTS: Patient (n = 1226) baseline characteristics included age (mean 68.0 years), virus-type (A 78.1%, B 21.9%), respiratory failure (57.2%), ICU admittance (14.4%), and NAI treatment within 5 days of illness (69.2%). Outcomes at 30 days included discharged (75.2%), subacute care (13.7%), acute care (4.5%), and death (6.6%). Two main clinical trajectories were identified, predictive by baseline scoring (mean ± SD, 4.3 ± 0.6 vs 3.5 ± 0.6, P < .001). Improved outcomes with NAI treatment within 5 days were indicated by significantly lower clinical status scores over time (unadjusted odds ratio [OR], 0.53; 95% confidence interval [CI], .41-.69; P < .001; adjusted OR, 0.62; 95% CI, .50-.77; P < .001, for baseline score, age, and within-patient correlations). In subanalysis, influenza vaccination was also associated with lower scores (adjusted OR, 0.67; 95% CI, .50-.90; P = .007). Analyses of binary endpoints showed insignificant results. CONCLUSIONS: The ordinal outcome scale is a potentially useful clinical endpoint for influenza therapeutic trials, which could account for the diverse clinical trajectories of hospitalized patients, warranting further development.
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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 | 0.023 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".