Bibliographic record
Abstract
Title: Clinical Decision Making in Paramedicine Author(s) & affiliation(s): Michael Eby – McMaster University, Hamilton, ON, Canada Sandra Monteiro – McMaster University, Hamilton, ON, Canada Geoffrey Norman – McMaster University, Hamilton, ON, Canada Walter Tavares – McMaster University, Hamilton, ON, Canada Background: Paramedics are frequently required to make rapid decisions in an uncontrolled, dynamic environment, often with limited diagnostic information. In Ontario, paramedic practice is based on a set of provincial medical directives that provide diagnostic and treatment criteria. Unsupervised deviation from these directives is classified as a form of error and highly discouraged. To date, there is little known about how years of clinical experience or level of certification affect the way these medical directives are used. The purpose of this study was to examine the relationship between paramedic experience, training and accuracy of treatment decisions when faced with patients who meet and fall outside of the existing medical directives. Methods: Thirty-one participants (16 experienced / 15 novice) were recruited from two paramedic services in Ontario. “Experienced” was defined as in-practice for 5 years or more. Participants were presented with 9 scenarios; in 6 scenarios, the patient presentation fit within the existing directives, while in 3 scenarios, the patient presentation fell outside the medical directives. Multiple-choice responses were used to capture participants’ decisions to treat or not treat the patients. Responses were scored and submitted to a mixed-factorial ANOVA to evaluate differences in accuracy between case types, years of experience and level of training. Results: There was a significant effect of case type (p < 0.004). Accuracy was lower when the patient presentation did not meet the criteria of the medical directive (76.34% (CI = 67.15% to 85.53%) vs. 98.35% (CI = 96.55% to 100%) when they did. There was no effect of years of clinical practice or level of certification. Conclusion: The results suggest both novice and experienced paramedics are able to accurately apply medical directives, however, there is a significant decrease in accuracy when the patient presentation does not fit one. This variation in practice may have a significant impact on patient safety, and further research is required to determine what factors may be causing this decreased accuracy.
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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 | 0.010 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".