Response to Letter to the Editor From Taguchi: “Osteonecrosis of the Jaw and Antiresorptive Agents in Benign and Malignant Diseases: A Critical Review Organized by the ECTS”
Bibliographic record
Abstract
We read the letter of Dr. Taguchi (1) about our article (2) with interest. Teeth and bones have some commonalities, such as containing large amounts of calcium and being the hardest substances in human body. Furthermore, teeth are embedded in bone, ie, the jawbone. Therefore, increased likelihood of bone disease in individuals with tooth disease and vice versa, as Dr Taguchi suggests, seems possible. On the other hand, several other conditions have been reported to be more common in osteoporotic patients, including atherosclerosis and cardiovascular disease (3, 4), diabetes, and other metabolic diseases (5). Although these coexistences may be due to the high prevalence of these diseases, especially in the aging, common pathogenetic mechanisms or even crosstalk between various organs and bone tissue may also exist. However, this does not automatically justify screening of all osteoporotic patients for, eg, coronary artery disease, in the absence of relevant symptoms or signs, and the same applies for dental disease. Nevertheless, we can only agree with Dr. Taguchi that regular dental check-up to maintain good oral health should be advised to everyone regardless of the presence of bone or other disease. Accumulated evidence indicates that the risk of osteonecrosis of the jaw (ONJ) depends on the potency and dose intensity of the antiresorptive agent. However, there are indeed 2 studies, 1 mentioned by Dr. Taguchi (6) and another (7), reporting no difference in ONJ incidence among patients taking raloxifene and alendronate. Of note, in the first study several sources of bias may have been introduced, and a similar hip fracture risk was found for alendronate and raloxifene (while no study to date has shown hip fracture reduction with raloxifene). The second study may have been underpowered to demonstrate a difference (25 vs 21 cases of ONJ). Since our article was a critical review, and the results of these studies contrast with a vast body of literature associating type, dose, and duration of antiresorptive therapy with risk of ONJ, we were reluctant in presenting such data that could mislead clinicians. The notion that osteoporosis itself may be an important risk factor for developing ONJ is intriguing despite the limited and mostly indirect evidence to support it. Since periodontal disease increases the risk for ONJ (2) and is more common in osteoporotic patients (1), this could be the link between the 2 conditions. Even so, this would by no means indicate that osteoporosis should not be treated for fracture prevention. osteonecrosis of the jaw A.D.A. received lecture fees from Amgen, Eli-Lilly, UCB and VIANEX; J.P. has nothing to disclose; N.N. has received consultant fees from UCB, speaker fee from ELi Lilly, and research support from Abiogen; A.P. declares having received honoraria in the past for lectures from Amgen; C.M. has nothing to declare; A.K. has received research funds from Alexion, Amgen, Ascendis, Chugai, Radius, Takeda, and Ultragenyx; M.C.Z. declares having received honoraria in the past for lectures or advice from Alexion, Amgen, Eli Lilly, Kyowa Kirin, Shire, and UCB; and J.J.B. has received consulting fees from Cole Pharma, Sandoz, Takeda, and UCB.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.015 | 0.014 |
| Insufficient payload (model declined to judge) | 0.009 | 0.011 |
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".