Impact of Cervical Collar and Patient Position on the Cerebral Blood Flow
Bibliographic record
Abstract
Background: Spinal protection during emergency medical service (EMS) transport after trauma has become a focus of debate. Historically, patients at risk for spine injury are transported in a rigid collar, long spineboard and headblocks. The cervical collar (c-collar) is hypothesized to provide stabilization for the cervical spine. However, little is known how the c-collar affects cervical blood flow. Methods: Cerebral blood flow was measured in multiple conditions using a non-invasive cerebral blood flow monitor to establish cerebral blood flow index (CBFI). The CBFI data were collected at: standing, sitting, 45°, 30°, 10° or 15°, and supine, with and without c-collar. Descriptive statistics were used for CBFI in each condition, and parametric statistical methods were utilized to determine the significance of changes in CBFI. Results: Five volunteers were recruited, and each tested in six positions with and without c-collar. Mean age was 49 (standard deviation (SD) 15) years and 60% were male. The CBFI mean of means was 71.0 with and 69.4 without the c-collar. Only one subject demonstrated a statistically significant difference in CBFI with c-collar. The CBFI mean of means for position was 72.6 for head of bed less than 30° and 68.1 for greater than 30°. All subjects demonstrated > 99% confidence for a statistically significant difference in CBFI when dichotomized using head of bed at 30°. Conclusions: Head of the bed position has greater influence on CBFI than the c-collar . Clinical significance in healthy volunteers is unknown but this change in cerebral blood flow may have clinical significance in traumatic brain injury or neurologic conditions that compromise autoregulation. J Neurol Res. 2020;10(5):177-182 doi: https://doi.org/10.14740/jnr611
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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 | 0.000 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".