Restoring ATBF: Dreaming the impossible dream?
Bibliographic record
Abstract
Dear Sir: Knowledge of the longterm effect of weight loss on adipose tissue blood flow (ATBF) is scarce. We read with great interest the paper by Rossi et al. [3], reporting the results of a study using laser-Doppler flowmetry (LDF), where baseline thigh ATBF was lower in morbidly obese subjects compared to ageand sex-matched controls (4.8 vs. 79.9 perfusion units /PU). These results confirm several previous studies [4]. After one-year follow-up subsequent to Roux-en Y gastric bypass (RYGB) in obese patients, they observed a meaningful weight loss (−40 kg, e.g.−28%). This weight loss was associated with a slight but nevertheless significant increase in ATBF (up to 10.0 PU) although patients remained obese. The main limitations of the Rossi et al. study are linked to the inherent drawbacks of the LDF method, relating mainly to calibration, multiple Doppler shifts, tissue optical properties, motion artefacts, biological zero and impossibility to express the results in absolute values [2]. The gold-standard for ATBF measurement is the 133 xenon wash-out method, which should be combined with LDF in order to validate data obtained therewith [1]. Anemia, a frequent complication of RYGB, and antihypertensive drugs, which may influence adipose tissue perfusion [4], could also interfere with LDF measurements. However, no indication relating to these specific issues was provided in the paper. Additionally, the chosen site of measurement stands out as another scientific issue, because subcutaneous adipose tissue in the thigh is known to be much less active than in abdomen [4]. Rossi et al.’s conclusion to the effect that the slight increase in ATBF, observed one year after RYGB, is rather negligible, is sustained by another study [5] reporting on ATBF following a very low-calorie diet
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".