Bibliographic record
Abstract
We welcome Mr. Suissa's comments on our paper regarding the role for long-term anticoagulation after acute thromboembolic limb ischemia.1 He makes good points, outlining some of the weaknesses of all retrospective studies, which we recognize in our paper. Although in principle I agree with his general epidemiologic arguments, I do not agree that patients with malignant disease or those who underwent amputation should be removed from our analysis. The purpose of our study was to analyze the natural history of patients who suffered from acute thromboembolic limb ischemia in the presence or absence of certain risk factors and long-term anticoagulation. Although patients in group 2 did contain a significant number of patients with malignant disease, these patients should be included in the outcome analysis because this is a recognized predisposing factor for recurrent venous and arterial thrombosis. In regard to the inclusion of the 4 patients who underwent early extremity amputation, it is important that these patients be included as they are at risk for recurrent arterial events, which was one of our main outcome variables. Suissa's comments are appreciated and serve to reinforce the limitations of all retrospective studies. However, these studies can propose trends that can be subsequently explored with prospective, randomized studies. Thomas L. Forbes, MD Division of Vascular Surgery University of Western Ontario London, Ont.
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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 | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".