Canadian Recommendations for Critical Care Ultrasound Training and Competency
Bibliographic record
Abstract
OBJECTIVE: To achieve national consensus on standards of training, quality assurance and maintenance of competence for critical care ultrasound for intensivists and critical care trainees in Canada using recently published international training statements. DATA SOURCES: Existing internationally endorsed guidelines and expert opinion. DATA SYNTHESIS: In November 2013, a day-long consensus meeting was held with 15 Canadian experts in critical care ultrasound in which essential topics relevant to training ultrasound were discussed. CONCLUSIONS: Consensus was achieved to direct training curriculum, oversight, quality assurance and maintenance of competence for critical care ultrasound. In providing the first national guideline of its kind, these Canadian recommendations may also serve as a model of critical care ultrasound dissemination for other countries.
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 | 0.029 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.010 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".