Severe Head Injury: Clinicians’ Awareness of the Literature
Bibliographic record
Abstract
OBJECTIVES: 1. To determine the awareness of the literature concerning therapeutic manoeuvres in severe closed head injury (CHI) among Canadian critical care clinicians and neurosurgeons, 2. To identify factors that affect utilization of these manoeuvres, and 3. To compare reported appropriateness and frequency of use with #1 and #2. METHODS: The study design was a systematic scenario-based survey of all neurosurgeons and critical care physicians treating patients with severe CHI in Canada. RESULTS: Fifty-nine of 99 neurosurgeons and 82 of 148 critical care physicians responded (57%). The majority of respondents were not able to identify the highest level of published evidence for most manoeuvres, except for the avoidance of corticosteroids (51%). The factor identified by most respondents as being most important in motivating use of any given manoeuvres was the level of published evidence (25%). Although reported appropriateness and frequency of use of most manoeuvres correlated well with each other, they did not correlate with awareness of evidence. In the case of corticosteroids, there was a strong correlation between non-use of steroids and awareness of evidence (R = -0.30, p = 0.0003). CONCLUSIONS: Respondents to this survey of Canadian physicians treating patients with severe head injury reported published evidence as being the most significant factor affecting use of a therapy. However, most respondents did not correctly identify the highest published level of evidence for most therapies. This study has identified difficulty with research translation that may have clinical implications.
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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 | 0.012 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".