A Modified Delphi Process to Develop Consensus Definitions of Time-Dependent Care by Paramedic Services Systems
Bibliographic record
Abstract
BACKGROUND: Just as prospective differentiation between true emergencies and calls for subacute patients is critical to the delivery of prehospital care, retrospective differentiation is critical to research and quality improvement. Determining the acuity of patients based on the type of care they received could complement the vital-sign-based instruments currently popular, yet imperfect. The study aim was to create a consensus definition of time-dependent care and a list of time-dependent interventions in paramedicine. METHODS: The study was a Delphi approach consisting of four rounds of voting by a bi-provincial panel of 22 Canadian key informants representing medical first responders, paramedics, and physicians - first to agree on a definition of time-dependent care - then to categorize 29 clinical and 34 pharmacological interventions. RESULTS: ," the panel reached consensus on 52 of 63 interventions (82.5%), of which 17 (32.7%) were voted time-dependent (11 clinical [64.7%] and six pharmacological [35.3%]). Clinical interventions included airway suction or de-obstruction, cricothyrotomy, positive pressure ventilation, chest decompression, cardiopulmonary resuscitation, defibrillation, cardioversion, pacing, and hemorrhage control. Pharmacological interventions included medication classed as sympathomimetics, caloric agents, antiarrhythmic agents, anticonvulsants, or tranquilizers. CONCLUSION: The panel reached a consensus on a definition of time-dependent care and used this to identify prehospital interventions that could serve as an instrument to improve care and system performance.
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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 | 0.320 | 0.241 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".