Retraction Statement: Bone Mineral Density Predicts Fractures in Chronic Kidney Disease
Post-publication record
OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.
Bibliographic record
Abstract
Retraction: West, S.L., Lok, C.E., Langsetmo, L., Cheung, A.M., Szabo, E., Pearce, D., Fusaro, M., Wald, R., Weinstein, J., and Jamal, S.A. (2015), Bone Mineral Density Predicts Fractures in Chronic Kidney Disease. J Bone Miner Res, 30:913–919. https://doi.org/10.1002/jbmr.2406. The above article, published online on 15 November 2014 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the journal's Editor-in-Chief, Professor Roberto Civitelli, the American Society for Bone and Mineral Research, and Wiley Periodicals, Inc. In 2016, the Women's College Hospital, University of Toronto, undertook an investigation into studies conducted by Dr Sophie Jamal. The investigation concluded that there was evidence of data manipulation. Whilst the investigation and resulting finding were not related to the above article, out of an abundance of caution, the authors undertook a thorough data quality review, including an independent data analysis, to ensure the integrity of the research. Through this process, inconsistencies in the data were found and it was determined that the results could not be fully replicated. As a result, the authors (except for Dr Jamal) requested retraction of their article. After review of the records, the ASBMR Research Integrity Panel, the Editor-in-Chief, and Wiley concur with the retraction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.163 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.015 | 0.022 |
| Insufficient payload (model declined to judge) | 0.012 | 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".