Bibliographic record
Abstract
We thank Drs. Watine and Bouarioua for their comments. They make three points on our recent review: that some studies of cancer survival were excluded; that there are studies where anemia did not remain in a regression model as an independent factor; and that the efficacy of erythropoetin in increasing survival remains unproven. We agree with the writers that reviews should be as comprehensive as possible and that inclusion and exclusion criteria should not arbitrarily reduce the evidence examined. However, to conduct a proper quantitative analysis (we did not carry out a qualitative review), particularly one that aims to provide credible estimates, it is necessary to ensure that the data are available, reasonably free of bias, and homogeneous enough to be poolable. Therefore, it was necessary to establish inclusion and exclusion criteria and stick with them even if this excluded some papers that might otherwise be used for other purposes. We excluded those where survival was not reported by anemia status and also articles where no information was presented on the number of patients with anemia. These are important criteria because the vast majority of studies were not aimed at the relation of survival to anemia; rather anemia, or hemoglobin, was just one of many factors included in analyses of some other relation such as toxicity and survival. Without information on the prevalence of anemia there is no way to assess whether the study had reasonable power to address this relation; it could simply be an example of a false negative result. And, if the corresponding survival data are not presented, there is no way to incorporate the paper in a quantitative meta-analysis. In relation to the role of anemia as an independent factor, it must be recognized that this is not something that can be proven on statistical grounds alone. In fact, the statistical significance of any factor in a multivariate regression depends on many things other than its true causal role. Very important among these are the order in which the factor is entered and the presence of other factors which correlate with the one of interest. For example, many of the studies cited examined the role of performance status, a measure that is very likely to be highly correlated with anemia; indeed, anemia may be one of the causes of poor performance. If performance status is entered in the model first because it is the object of the study, then anemia may not remain in the model as an independent factor despite actually being an important determinant. This sort of problem makes it impossible to draw conclusions on the true independent effect of anemia on survival based simply on the number of studies in which it was left in a regression model. This is the case in two 1, 2 of the papers cited in the table and included in our review. In the other two,3, 4 lower hemoglobin was clearly a significant factor in both univariate and multivariate analyses. Finally, given the strong association of anemia with poorer survival—and the high clinical credibility of this relation—we suggested that treatment to correct anemia should be considered, but we did not claim that this has been established. Clearly, to do so requires appropriate clinical trials. J. Jaime Caro M.D.*, Maribel Salas M.D., D.Sc. , Alexandra Ward Ph.D. , Glenwood Goss M.D. , * Division of Internal Medicine, McGill University Montreal, Canada Caro Research Boston, Massachusetts, Caro Research Boston, Massachusetts, Ottawa Regional Cancer Center Ottawa, 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 | 0.011 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.025 | 0.044 |
| Insufficient payload (model declined to judge) | 0.028 | 0.020 |
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".