Notice of Retraction: Predicting falls among older persons using machine learning [abstract]
Bibliographic record
Abstract
This abstract has been retracted at the request of the authors. The authors noted that the abstract is in breach of the following ethical principles: • Ethical approval was not obtained for the published conference abstract. This occurred due to a misinterpretation by the following authors — Carter Rhea, Gustavo Duque, Andrea Faust and Salam Bouhabel — who believed the project was already covered under a prior ethics approval number.• Ben Kirk and Myrla Sales were erroneously listed as co-authors on the abstract. They were not informed of the submission, did not provide consent to be included as senior or co-authors, and received no correspondence from the co-authors or the scientific conference.• Ben Kirk was first made aware of the published abstract on 21 July 2025, approximately 10 months after its publication in September 2024, via communication from the Director of the Western Health Office for Research—Ethics & Governance. The authors deeply regret that this situation has occurred and offer a sincere apology to the Journal, the Western Health Office for Research, and the broader research community. The Editor supports this retraction and regrets any inconven-ience caused to the readers. The original abstract remains online to maintain the scholarly record but has been watermarked as retracted.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Research integrity Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | medium |
| gpt | Research integrity Domain: not available · Genre: Editorial About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.010 | 0.169 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.125 | 0.073 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".