Letter to the Editor From Watts and Leslie: Comparisons Between Different Antiosteoporosis Medications on Postfracture Mortality: A Population-based Study
Bibliographic record
Abstract
In their recent article, “Comparisons between different antiosteoporosis medications on postfracture mortality: a population-based study.” Wu et al (1) state that “the usage of osteoporotic medication…may lower postfracture mortality.” They use the word “may,” which does not preclude “may not.” In our view, they underestimate the importance of treatment selection bias and especially channeling bias, a form of allocation bias in which drugs with similar therapeutic indications are prescribed to patients with prognostic differences, with the consequence that outcomes may be incorrectly attributed to the drug. Without a rigorous strategy for examining and addressing these biases, no conclusions can be drawn. They compared women users of bisphosphonates and denosumab with those using raloxifene and bazedoxifene. Yes, they found lower mortality in bisphosphonate and denosumab users, but the difference may be more related to who prescribes these drugs (internists vs gynecologists) and by extension who gets these drugs. Their findings in men—higher mortality with denosumab (after any fracture) and ibandronate (after vertebral fracture)—are astounding. Their comparator group is zoledronic acid (raloxifene and bazedoxifene are not used in men)—the only antiosteoporosis drug shown in a placebo controlled trial to reduce mortality risk. The very short duration of medication exposure (mean 1.05 years in survivors vs 0.76 in nonsurvivors) hardly seems sufficient to affect long-term survival. Survivors started treatment later than nonsurvivors (22.42 months vs 15.34 months)—paradoxically suggesting that delaying treatment may actually be protective. Moreover, this might shorten the observation time, and therefore potential for death, among the former. Leslie et al (2) found that women in Manitoba who had a bone density test had lower mortality compared with age-matched controls. It is unlikely that having the bone density test is the reason, but rather the selection of healthier women for investigation and treatment for osteoporosis. Only a randomized trial with an appropriate control group can establish a causal relationship between treatment and outcomes, such as mortality. To that end, Cummings et al (3) performed a meta-analysis of randomized placebo-controlled clinical trials of drug treatments for osteoporosis that included 101 642 unique participants. No significant association was found between mortality and all drug treatments (38 clinical trials) or a bisphosphonate treatment (21 clinical trials). Given such strong evidence, observational studies, such as that of Wu et al, provide little new information or understanding but may add to confusion (4). The authors have nothing to disclose.
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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 | 0.008 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".