Bibliographic record
Abstract
FigureIt is with profound sorrow that we announce the passing of our friend and colleague, Dr David Stather, who died in a sky-diving accident in Arizona on January 25, 2014 at the age of 41. Dr Stather was an Interventional Pulmonary Medicine (IPM) specialist at the University of Calgary. He was widely recognized as a passionate and energetic colleague who practiced medicine to the highest clinical and academic standards. He cared deeply about the well-being of his patients, many of whom had advanced respiratory diseases; David made a difference in their lives with his commitment, expertise, and compassion. Dr Stather’s areas of academic expertise included simulation teaching of thoracic procedures; he had published a number of scientific papers in this area and was widely regarded as an international opinion leader in the field. He was also actively developing a novel bronchoscopic approach toward the treatment of pulmonary aspergilloma. He directed and greatly improved the Calgary IPM training program, from which he was himself the first graduate in 2006. He was an active member of the Canadian Thoracic Society clinical assembly on chest procedures, the American Association of Bronchology and Interventional Pulmonology (AABIP) as well as the Association of Interventional Pulmonology Program Directors (AIPPD). David certainly had much more to contribute to this field, but his legacy will live on through the fellows he has trained, and through his colleagues who will endeavor to continue his work. In recognition of Dr Stather’s many outstanding contributions to the field, we are proud to be establishing the Dr David Stather Memorial Award, which will be used to support IPM Fellowship Training in Calgary (https://netcommunity.ucalgary.ca/davidstather). Alain Tremblay, MD Paul MacEachern, MD Alex Chee, MD University of Calgary, Calgary, AB, Canada
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.126 | 0.049 |
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".