寃쎌텛 �넀�긽 �솚�옄�뿉�꽌 �쟾�궛�솕 �떒痢� 珥ъ쁺 �떆�뻾�쓣 �쐞�븳 �엫�긽�쟻 湲곗�
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
Purpose: National Emergency X-Radiography Utilization Study (NEXUS) criteria and the Canadian Cervical Spine rule (CCR) are commonly used in cervical trauma patients to determine whether a plain cervical X-ray should be performed. However, plain cervical X-rays are so inaccurate that cervical spine computed tomography (CT) is often considered as a screening test. We studied the usefulness of the NEXUS criteria and the CCR for determining the need for a CT evaluation in the emergency department (ED).\n \nMethods: This prospective observational study was conducted from January 2007 to March 2008. Plain Xray and CT scans of the cervical spine were performed on blunt trauma patients with neck pain. The relevancy of CT was examined using the NEXUS criteria and the CCR. Sensitivity, specificity, positive predicted value, and negative predicted value analyses were performed to diagnose the cervical spine injury.\n \nResults: During the study period, 284 patients were enrolled in this study. The sensitivity, specificity, positive predicted value, and negative predicted value of the NEXUS criteria were 87.5%, 1.1%, 5.0%, and 60.0% respectively, while those of the CCR were 87.5%, 8.2%, 5.3%, and 91.6%. There were two missed fracture cases when the NEXUS criteria and the CCR were applied independently, however, no cases were missed when both were applied.\n \nConclusion: This study suggests the NEXUS and the CCR in combination can be used as a guide to CT evaluation for cervical spine injury in the ED.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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