Covid vaccines: Canada to dispose of 13.6 million AstraZeneca doses owing to lack of demand
Bibliographic record
Abstract
Objectives: To determine interobserver variability between an emergency medicine consultant and nurse practitioners for the use of the Wells score in the assessment of deep vein thrombosis (DVT) in the emergency department. Methods: A prospective cohort study was undertaken in a population of 100 cases of suspected DVT. The Wells score reading from the consultant was compared with the reading of the nurse practitioners. Consultant and nurses were blinded to each other’s assessments. The nurse practitioners were trained in interpreting the Wells score by assessing 100 patients together with the consultant before the start of the study. Results: Consultant and nurse practitioner assessments resulted in the same final Wells score in 81% of cases (simple agreement), with a kappa score of 0.74 (95% CI 0.63 to 0.84). If the nurse practitioner score had been followed in preference to the consultant assessment, this would have resulted in eight patients being assessed in a lower risk algorithm (8%). Conclusion: There is good interobserver agreement between consultant and nurse practitioners for the use of the Wells score as part of a DVT assessment service within the emergency department. Pretest scoring is pivotal to integrated strategies for the exclusion of DVT. The Wells score is a robust and reliable tool for pretest scoring in the emergency department regardless of the grade of the assessor, provided there is adequate training in its use.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.204 | 0.066 |
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".