Bibliographic record
Abstract
The paper of Lundby et al. (2007) on blood volume changes by injection of erythropoietin (Epo) during a relatively long period of 15 weeks is very interesting but raises a number of questions concerning Methods and Discussion. This is a multicentre investigation with scientists in Denmark, Canada, Greece, Spain and France. The subjects seem to be Danish and investigated in Denmark, but this is not clearly stated. The state of physical training as well as physical activity before and during the 15 weeks of erythropoietin injections is not communicated. Since endurance training markedly influences plasma and red cell volume, this lack of information is important. The authors have determined total haemoglobin mass, but they present only the derived quantities red cell volume and plasma volume. The former is additionally influenced by the high water content of young red cells during the period with the most marked reticulocytosis (weeks 5 through 7); the calculation of the latter from haemoglobin mass depends on buffy coat, trapped plasma and whole body haematocrit, which are not specified. Possible variations of whole body haematocrit might be caused by changes of vascular tone mentioned in the Discussion. The Discussion treats mainly the causes of the calculated plasma volume reduction during Epo application. One mentioned mechanism is vasoconstriction induced by Epo without specifying how it works (enforced filtration of fluid across the capillaries?). But the resulting changes in small vessel haematocrit (Burge & Skinner, 1995) might also have repercussions on venous haematocrit influencing the calculation of plasma volume. The main factor for plasma volume regulation, namely the filling state of the atria of the heart influencing the secretion of atrial natriuretic peptides, renin–angiotensin–aldosterone and adiuretin is not mentioned. Increased blood volume sensed by distension of the atrial wall causes diuresis and thus reduction of the plasma volume. Even if no blood volume change exists according to the authors' calculation, the increased vascular tone might result in a blood shift to the heart. The tendency for an increase in stroke volume and cardiac output fits such a mechanism. Finally the authors emphasize the importance of increases in haemoglobin concentration and haematocrit on oxygen transport, arguing that the flexibility of the erythrocytes in their experiments and thus blood viscosity was unchanged. But increases of haematocrit increase blood viscosity per se; they are only advantageous for oxygen transport if the original haematocrit is lower than the optimal haematocrit (e.g. Gaehtgens et al. 1979). Above this value cardiac work increases, and thus the maximal cardiac output must decrease. Many investigations have demonstrated that haematocrit variations are only weakly correlated with maximal oxygen uptake (e.g. Böning et al. 2004; El-Sayed et al. 2005). Studies at altitude have even shown that the best adapted populations (Tibetans, Ethiopeans) possess no marked increase in haemoglobin concentration (Beall et al. 2002). The increased cardiac output with low viscosity costs no more energy than a lowered cardiac output with high viscosity at a given oxygen uptake. But lowered blood pressure and risk of thrombosis if haematocrit remains near sea level values are an advantage for survival at altitude.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".