The Swine Flu Triage (SwiFT) study: development and ongoing refinement of a triage tool to provide regular information to guide immediate policy and practice for the use of critical care services during the H1N1 swine influenza pandemic
Bibliographic record
Abstract
OBJECTIVES: To use, existing critical care and early pandemic, data to inform care during the pandemic influenza A 2009 (H1N1) pandemic (with a possible use for triage - if the demand for critical care seriously exceeded supply). To monitor the impact of the H1N1 pandemic on critical care services, in real time, with regular feedback to critical care clinicians and other relevant jurisdictions to inform ongoing policy and practice. DESIGN: Modelling of data and cohort study. SETTING: Modelling - 148 adult, general critical care units in England, Wales and Northern Ireland in the Intensive Care National Audit & Research Centre Case Mix Programme. Cohort study - 192 acute hospitals in England, Wales, Northern Ireland, Scotland and the Republic of Ireland. PARTICIPANTS: Modelling - 105,397 admissions to adult, general critical care units. Cohort study - 1728 H1N1 pandemic-related admissions referred and assessed as requiring critical care. MAIN OUTCOME MEASURES: Modelling - requirement for organ support and acute hospital mortality. Cohort study - survival to the end of critical care. RESULTS: Modelling - cancelled or postponed, elective or scheduled surgery resulted in savings in calendar days of critical, Level 3 and advanced respiratory care of 17, 11 and 10%, respectively. These savings varied across units. Using routine, physiological variables, the best triage models, for all and for acute respiratory admissions, achieved only satisfactory concordance of 0.79 and 0.75, respectively. Application of the best model on all admissions indicated that approximately 12.5% of calendar days of critical care could be saved. Cohort study - research governance approvals were achieved for 192 acute hospitals, for 91 within 1 day of central research and development approval across the five countries. A total of 1725 cases (562 confirmed) were reported. Confirmed cases were young (mean age of 40 years), had low severity of acute illness on presentation [61% CURB-65 (confusion, urea, respiratory rate, blood pressure, age over 65 years) 0-1], but had long stays in critical care (median 8.5 days) and were likely to be ventilated (77% for median 9 days). Risk factors for acute hospital death were similar to those for general critical care admissions. CONCLUSIONS: SwiFT was rapidly established. Models based on routine physiology suggested limited value for triage. More data and further modelling are warranted. The magnitude of the pandemic did not approach the worst-case scenario modelling, and UK-confirmed H1N1 cases appeared similar to those reported internationally. FUNDING: The National Institute for Health Research Health Technology Assessment programme.
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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 | 0.028 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".