Letter to the editor regarding “Efficacy of adding ramipril (VAsotop) to the combination of furosemide (Lasix) and pimobendan (VEtmedin) in dogs with mitral valve degeneration: The VALVE trial”
Bibliographic record
Abstract
reason, it would be important to know the average maximum furosemide dosage received, as well as the number of dogs that experienced renal compromise, were euthanized, or exited the study for polyuria and polydipsia or worsening renal dysfunction.It would also be relevant to know the number of cases accrued from each institution, as well as the average furosemide dosage, number of dogs receiving spironolactone or doubled ramipril dosage, and how these potential institutional differences were accounted for during data analysis.A large number of cases from a single center may diminish the benefits of a multicenter study design or raise the possibility of unintentional institutional acquisition bias.Although the VALVE results are surprising and interesting, a conclusion that they obviate the need for ACEI treatment in the management of CHF from MMVD is not justified.We believe this study to be hypothesis generating, rather than pivotal.A larger study, employing methodology that provides evidence of more complete RAAS inhibition with contemporary adjunctive heart failure treatment is needed to provide pivotal data upon which to guide practice.
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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 | 0.004 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.019 | 0.018 |
| Insufficient payload (model declined to judge) | 0.006 | 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".