A Canadian national survey of the medical management of acute traumatic spinal cord injury
Bibliographic record
Abstract
Background: The management of patients with acute traumatic spinal cord injury (SCI) remains a significant challenge, with ongoing debate surrounding the optimal targets for mean arterial pressure (MAP), spinal cord perfusion pressure (SCPP), and hemoglobin (Hb) transfusion thresholds. This study aimed to identify areas of consensus and discordance in the management strategies employed by Canadian healthcare providers caring for patients with acute SCI. Methods: A comprehensive multi-stage survey was developed and administered to healthcare providers actively involved in the management of acute SCI, including neurosurgeons, orthopedic surgeons, intensive care specialists, trauma surgeons, and emergency medicine physicians. The survey assessed preferences related to MAP, SCPP, and Hb transfusion thresholds, as well as opinions on the need for future research in this area. Results: A total of 71 healthcare providers completed the survey, with a 100% completion rate. The majority of participants were from neurosurgery (38.1%), intensive care (31.0%), and orthopedics (25.4%). While 75.7% of participants routinely set a MAP target, only 7.1% set an SCPP target. The most common Hb transfusion threshold was <7 g/dL (50.7%) for patients with neurological deficits, with the majority (62.3%) maintaining this threshold for all patients. A significant proportion (15.9%) would consider transfusing based on clinical status alone, regardless of the Hb level. Two-thirds of participants (66.7%) believed the current equipoise in transfusion targets warrants a randomized controlled trial (RCT), and 79.5% of these respondents indicated a willingness to enroll patients. Conclusions: This survey highlights the significant variability in the management of acute traumatic SCI, particularly regarding MAP, SCPP, and Hb transfusion thresholds among Canadian healthcare providers. The findings underscore the need for the development of evidence-based guidelines and the implementation of multicenter RCTs to establish best practices and optimize the care of this complex patient population.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".