Bibliographic record
Abstract
We are thankful to Dr Wang [1] for his positive comments on our manuscript as well as his very pertinent question. As he pointed out, we observed a higher rate of stroke in the immediate postoperative period in the group with residual aortic regurgitation (AR), which, however, did not reach statistical significance [2]. Our study was aimed at assessing the impact of residual AR on long-term mortality, for which we have complete data accessible through the Quebec Civil Registry. Unfortunately, long-term rates of complications such as strokes are less readily available, but we do have long-term follow-up data for 64.6% of the AR ≤1 group (median follow-up 4.6 years) and 76.3% of the group with AR >1 (median follow-up 4.7 years). Analysis of those sub-groups revealed no statistically significant difference in freedom from neurological events at 1 (97.3 vs 97.9%), 3 (95.5 vs 95.5%), 5 (93.2 vs 95.5%) and 10 years (89.1 vs 90.3%), respectively (P = 0.72). Of note, a similar incidence of stroke was observed despite a higher percentage of patients receiving mechanical valves in the AR >1 group than in the AR ≤1 group (28.4 vs 6.4%, P < 0.01). The mechanism suggested by Dr Wang to explain potentially higher rates of stroke in patients with residual AR in transcatheter aortic valve implantation [3] remains intuitive and, although not supported by this post hoc sub-group analysis with its inherent limitations, should be verified in a larger study.
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 | 0.005 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.023 | 0.048 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".