Comparison of different methods of measurement of aspirin resistance: using the appropriate statistic: reply
Bibliographic record
Abstract
We thank Dr Lotrionte and coworkers for their interest in our work and their suggestion to use Bland–Altman analysis of agreement to complement the results presented in our original paper. 1 We agree with Lotrionte et al. that the Bland–Altman analysis of agreement is most useful in comparing two measurements of the same phenomenon, say a mass volume compared by ultrasonography and CT scan. 2 However, we must point out a major difference between such an analysis and the one presented in our paper. We compared tests that do not analyse the same phenomenon, and do not report results in a directly comparable way. In a paper published in 2003, Bland and Altman state that regression analysis in the evaluation of agreement is appropriate when two methods of measurement have different units. 3 Indeed, Bland and Altman argue that, as one type of measurement could not be simply replaced by the other, the most suitable analysis would be to predict one result by the other through regression. Accurately predicting one result by the other would allow to conclude on adequate agreement between the methods.
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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 | 0.029 | 0.168 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.002 | 0.009 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.028 | 0.048 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".