Can Undergraduate Paramedic and Nursing Students Accurately Estimate Patient Age and Weight?
Bibliographic record
Abstract
INTRODUCTION: Accurate estimation of a patient's age and weight are skills expected of all healthcare clinicians, including paramedics and nurses. It is necessary because patients may be unable to communicate such information due to unconsciousness or an altered state of conscious. Age and weight estimation influence calculation for medication dosages, defibrillation, equipment sizing, and other invasive procedures such as intubation. The objective of this study was to identify whether undergraduate paramedic and nursing students were able to accurately estimate a patient's age and weight based on digital patient photos. METHODS: A prospective, observational study involving undergraduate paramedic and nursing students from two Australian universities was used to estimate the age and weight of seven patients (adult and pediatric). Each patient image appeared in a PowerPoint presentation for 15 seconds, followed by a short pause, with the next patient image commencing automatically. RESULTS: The findings demonstrated variable accuracy in age and weight estimation of the patients. Age estimations of pediatric patients were more accurate than estimations for adult patients. The majority of patient weights were under-estimated, with university undergraduate students in one university displaying similar estimations to the other university counterparts. CONCLUSIONS: Results from this study identified variations in students' ability to accurately estimate a patient's age and weight. This study shows that consideration should be given to age and weight estimation education, which could be incorporated into undergraduate healthcare curriculum.
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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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".