Prospective Development and Validation of a Weight Prediction Tool for Patients With Obesity Undergoing Critical Care Transport Using Width and Arm Circumference
Bibliographic record
Abstract
Objective Obesity rates are rising, affecting health care systems and causing potential delays in critical care transfers. Accurate weights are important for critical care transport to ensure the appropriate transport asset is selected and dispatched in a timely fashion. Inaccurate weight measurements can lead to various unnecessary delays, including not sending appropriate nonbariatric assets, delays due to secondary dispatch, and delays due to systemic overuse of these bariatric capable assets. The primary objective of this study was to develop and validate a weight prediction tool for patients with obesity undergoing critical care transport. The secondary objective was to compare the performance of this model to the Crandall weight prediction tool. Methods A prospective observational study was conducted, collecting data from patients transported by Ornge air ambulance between May 2022 and April 2023. Adults weighing >100 kg undergoing interfacility transfers were included. Four predictive models, using height, arm circumference, width, and girth measurements, were evaluated against actual patient weights and analyzed separately for males and females. Model performance was evaluated by mean squared error, mean absolute error (MAE), mean absolute percentage error, R 2 , and F-statistic. Results The Ornge model using arm circumference and width demonstrated the highest accuracy and stability in predicting patient weight for both males and females. This model exhibited the lowest MAE of 12.2 kg, was within a margin of 20% error 91.2% of the time, and had an overall false negative error of 6.9%, outperforming all other models. Conclusion The Ornge arm circumference and width-based model offers a reliable method for predicting patient weight in air ambulance settings. Implementing this tool could improve the efficiency and safety of patient transfers by reducing delays caused by inaccurate weight estimations, thereby expediting access to critical care. Further research is recommended to validate these findings in larger and more diverse populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".