Accuracy of Offspring-Reported Parental Hip Fractures: A Novel Population-Based Parent-Offspring Record Linkage Study
Bibliographic record
Abstract
The objective of this study was to test the validity of offspring-reported parental hip fracture in a unique bone mineral density (BMD) registry linked to administrative databases spanning 4 decades. Population-based data were from Manitoba, Canada, and included hospital abstracts, health insurance registrations, and the provincewide BMD registry. The cohort included individuals aged ≥40 years with BMD tests and self-reports of parental hip fracture between 2006 and 2014. Population registry data for 1966-2014 were used to link offspring with their parents, and hospital records were used to ascertain parental fractures. Overall, 8,112 offspring met the inclusion criteria; 13.6% had a parental hip fracture diagnosis in administrative data during an average of 32.9 years of follow-up. Agreement between parental hip fracture from offspring reports and diagnoses in administrative data was good (κ = 0.68). The sensitivity of offspring reports was 0.70 (95% confidence interval: 0.67, 0.73), and specificity was 0.96 (95% confidence interval: 0.96, 0.97). Offspring characteristics associated with disagreement included male sex, northern rural residence, early BMD test year, and longer interval between BMD test and parental hip fracture diagnosis. This proof-of-concept study focused on hip fractures, but use of record linkage techniques to validate offspring-reported parental information can be extended to other conditions.
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".