An Emergency Medical Technician Administered Falls-Assessment Protocol to Safely Identify Elderly Adults with Non-Urgent Conditions that may Avoid Transport to Emergency Department
Bibliographic record
Abstract
Background: Approximately two-thirds of patients transported to emergency departments (ED) for a fall are discharged from the ED without urgent treatment. This pilot study tests the feasibility of implementing a pre-hospital falls-assessment protocol performed by emergency medical technicians (EMTs) to determine whether a patient who fell needs an ED assessment or could be referred safely to a community resource. Methods: The protocol was administered by trained EMTs to adults aged ≥ 65 after a fall between October 2019 and March 2020 in Sherbrooke (QC). All patients were transported to ED regardless of protocol outcome (transport recommended/not recommended). The objective was to assess if EMTs could complete the protocol and make the appropriate decision concerning the transport to ED. Secondary objectives aimed to assess the accuracy in identifying patients who do not require transport, and to measure the impact on avoidable ambulance transports. Results: A total of 125 EMTs interventions were carried out: 17 patients were in the transport not recommended group, representing 14% of transport to hospital for falls-related EMTs calls that could be possibly avoided. Of these, 110 were transported to ED. Mean duration of on-site EMTs interventions was of 31 minutes. Forty-seven patients were admitted, mostly for infections and fractures, including four in the transport not recommended group. Conclusions: This study showed that EMTs can administer a falls-assessment protocol aimed at identifying patients that need an ED evaluation. Results permitted to amend the protocol before the second phase of the project evaluating the safety of the protocol.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".