Invoking the “expectant” triage category: Can we make the paradigm shift?
Bibliographic record
Abstract
Medical triage is the process of determining the priority of patients' treatments based on the severity of their condition. Triage provides the healthcare provider the ability to identify the most urgent cases first, with the goal of maximizing each individual patient's outcome. When resources are challenged, such as in a disaster, the healthcare provider's goal becomes to maximize overall population survival. In this context, the triage process must identify patients who require resources urgently, as well as those who have the best chance of survival. The revised triage process must include an "expectant management" category, to identify patients for whom further resuscitation is delayed, as they have a poor chance of survival and require significant resources. The paradigm shift that is required in these circumstances can be challenging for pediatric healthcare providers. Many may find themselves unable to change the decision-making process that would favor overall survival and best outcome for the most members of a population, while potentially not addressing the most sick or injured because they have low chances of survival. We hypothesized that participating in a multiprofessional ethics-based educational session regarding making difficult triage decisions may improve participants' perceived ability to use the "expectant" triage category in a disaster setting. Participants took part in an ethics-based educational session and completed a pre- and postsurvey. Results demonstrated a significant change in the participants' self-perceived comfort level using the disaster triage tools and improved their confidence to use the expectant triage category in a disaster setting.
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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 | 0.047 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.007 | 0.018 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".