Lack of CT scanner in a rural emergency department increases inter-facility transfers: a pilot study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: Rural emergency departments (EDs) are an important gateway to care for the 20% of Canadians who reside in rural areas. Less than 15% of Canadian rural EDs have access to a computed tomography (CT) scanner. We hypothesized that a significant proportion of inter-facility transfers from rural hospitals without CT scanners are for CT imaging. Our objective was to assess inter-facility transfers for CT imaging in a rural ED without a CT scanner. RESULTS: We selected a rural ED that offers 24/7 medical care with admission beds but no CT scanner. Descriptive statistics were collected from 2010 to 2015 on total ED visits and inter-facility transfers. Data was accessible through hospital and government databases. Between 2010 and 2014, there were respectively 13,531, 13,524, 13,827, 12,883, and 12,942 ED visits, with an average of 444 inter-facility transfers. An average of 33% (148/444) of inter-facility transfers were to a rural referral centre with a CT scan, with 84% being for CT scan. Inter-facility transfers incur costs and potential delays in patient diagnosis and management, yet current databases could not capture transfer times. Acquiring a CT scan may represent a reasonable opportunity for the selected rural hospital considering the number of required transfers.
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.
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 | 0.001 | 0.003 |
| 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 it