A retrospective cohort pilot study to evaluate a triage tool for use in a pandemic
Bibliographic record
Abstract
INTRODUCTION: The objective of this pilot study was to assess the usability of the draft Ontario triage protocol, to estimate its potential impact on patient outcomes, and ability to increase resource availability based on a retrospective cohort of critically ill patients cared for during a non-pandemic period. METHODS: Triage officers applied the protocol prospectively to 2 retrospective cohorts of patients admitted to 2 academic medical/surgical ICUs during an 8 week period of peak occupancy. Each patient was assigned a treatment priority (red -- 'highest', yellow -- 'intermediate', green -- 'discharge to ward', or blue/black -- 'expectant') by the triage officers at 3 separate time points (at the time of admission to the ICU, 48, and 120 hours post admission). RESULTS: Overall, triage officers were either confident or very confident in 68.4% of their scores; arbitration was required in 54.9% of cases. Application of the triage protocol would potentially decrease the number of required ventilator days by 49.3% (568 days) and decrease the total ICU days by 52.6% (895 days). On the triage protocol at ICU admission the survival rate in the red (93.7%) and yellow (62.5%) categories were significantly higher then that of the blue category (24.6%) with associated P values of < 0.0001 and 0.0003 respectively. Further, the survival rate of the red group was significantly higher than the overall survival rate of 70.9% observed in the cohort (P < 0.0001). At 48 and 120 hours, survival rates in the blue group increased but remained lower then the red or yellow groups. CONCLUSIONS: Refinement of the triage protocol and implementation is required prior to future study, including improved training of triage officers, and protocol modification to minimize the exclusion from critical care of patients who may in fact benefit. However, our results suggest that the triage protocol can help to direct resources to patients who are most likely to benefit, and help to decrease the demands on critical care resources, thereby making available more resources to treat other critically ill patients.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".