Requesting Wrist Radiographs in Emergency Department Triage
Bibliographic record
Abstract
Crowding is extremely problematic in Canada, as the emergency department (ED) utilization is considerably higher than in any other country. Consequently, an increase has been noted in waiting times for patients who present with injuries of lesser acuity such as wrist injuries. Wrist fractures are the most common broken bone in patients younger than 65 years. Many nurses employed within EDs are requesting wrist radiographs for patients who present with wrist complaints as a norm within their working practice. Significant potential advantages can ensue if EDs adopt a triage nurse-requested radiographic protocol; patients can benefit from a significant time-saving of 36% in ED length of stay (M. Lindley-Jones & B. J Finlayson, 2000)— when nurses initiated radiographs in triage. In addition, the literature suggests that increased rates of patient and staff satisfaction may be achieved, without compromising quality of radiographic request or quality of service (W. Parris,S. McCarthy, A. M. Kelly, & S. Richardson, 1997). Studies have shown that nurses are capable of requesting appropriate radiographs on the basis of a preset protocol. As there are no standardized set of rules for assessing patients, presenting with suspected wrist fractures, a training program as well as a diagnostic algorithm was developed to prepare emergency nurses to appropriately request wrist radiographs. The triage nurse-specific training program includes the following topics: wrist anatomy and physiology, commonly occurring wrist injuries, mechanisms of injury, physical assessment techniques, and types of radiographic images required. The triage nurse algorithm includes the clinical decision-making process. Providing triage nurses with up-to-date evidence-based educational material not only allowed triage nurses to independently assess and request wrist radiographs for patients with potential wrist fractures but also strengthening the link between competent nursing care and better patient outcomes. A review of the literature also found that such initiatives increase patient and staff satisfaction as well as promoting efficient use of right staff at the right time.
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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".