Bibliographic record
Abstract
The investigators from University of Auckland indicate that promoting the use of calcium supplements to post-menopausal women may not be advisable. However, the report of their study,1 which we evaluated in our review,2 included the following text: “The present data do not permit definitive conclusions to be reached. . . .”. This was also the conclusion that we and others reached.3,–7 It cannot be ignored that providing 1000 mg/d of a highly bioavailable calcium supplement to participants with 861 ± 390 mg/d baseline dietary calcium intake might put some of them at risk of exceeding the upper intake levels for calcium (2500 mg/d). The mean baseline of calcium intake in most randomized controlled trials (RCTs) is higher than the estimated mean intake of the US population aged 50 years and over.8 The threshold behavior of calcium9 might explain why marginal benefits on the skeleton have been reported in those RCTs. Hence, the probability of bone-beneficial effects of supplemented calcium in individuals with very low baseline calcium intakes cannot be evaluated when participants in most RCTs already had sufficient calcium intake. We agree with Grey et al. that physicians should carefully consider calcium supplementation on an individual basis, based on estimating an individual's usual calcium intake and other compromising factors. If the recommended calcium intake cannot be achieved from food, calcium supplement can be recommended. We would like to emphasize that the findings of Bolland et al.1 cannot be generalized to all postmenopausal women, as their participants were older (74.2 ± 4.2).
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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 | 0.006 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.023 | 0.025 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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".