Bibliographic record
Abstract
OBJECTIVE: The goal of this search was to review the current literature regarding paramedic triage of primary care patients and the safety of paramedic-initiated non-transport of non-urgent patients. METHODS: A narrative literature review was conducted using the Medline (Medline Industries, Inc.; Mundelein, Illinois USA) database and a manual search of Google Scholar (Google; Mountain View, California USA). RESULTS: Only 11 studies were found investigating paramedic triage and safety of non-transport of non-urgent patients. It was found that triage agreement between paramedic and emergency department staff generally is poor and that paramedics are limited in their abilities to predict the ultimate admission location of their patients. However, these triage decisions and admission predictions are much more accurate when the patient's condition is the result of trauma and when the patient requires critical care services. Furthermore, the literature provides very limited support for the safety of paramedic triage in the refusal of non-urgent patient transport, especially without physician oversight. Though many non-transported patients are satisfied with the quality of non-urgent treatment that they receive from paramedics, the rates of under-triage and subsequent hospitalization reported in the literature are too high to suggest that this practice can be adopted widely. CONCLUSION: There is insufficient evidence to suggest that non-urgent patients can safely be refused transport based on paramedic triage alone. Further attempts to implement paramedic-initiated non-transport of non-urgent patients should be approached with careful triage protocol development, paramedic training, and pilot studies. Future primary research and systematic reviews also are required to build on the currently limited literature. Fraess-Phillips AJ . Can paramedics safely refuse transport of non-urgent patients? Prehosp Disaster Med. 2016;31(6):667-674.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".