Letter to the Editor and Reply: Sedation-assisted Orthopedic Reduction in Emergency Medicine: The Safety and Success of a One Physician/ One Nurse Model
Bibliographic record
Abstract
To the Editor: We applaud Vinson and Hoehn for eloquently demonstrating that the performance of sedation assisted procedures in the emergency department (ED) does not necessarily require a 2 physician team. From a Canadian perspective, where single physician coverage in smaller EDs is common, this has important implications in terms of efficiency of patient care, reduction in the need for patient transfer and decreasing the time to definitive treatment for ED patients. We would like to draw attention to a model of care practiced in Halifax, Nova Scotia for over 15 years, using a team consisting of an advanced care paramedic (ACP) and a single physician, the former to conduct the sedation, and the latter to do the procedure.1 The skills of ACPs complement specific supplementary training in Procedural Sedation and Analgesia (PSA) to produce, in our opinion, expert ED sedationists, and our database of over 4000 safely conducted PSAs attest to this. Although performing PSA is primary role of ACPs in our ED, success with this has expanded our use of paramedics to a number of other ED tasks, freeing up other staff to perform what they do best.2
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 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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.026 | 0.025 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".