FP447INCIDENCE OF FRACTURE IN KIDNEY TRANSPLANTATION ACCORDING TO THE FOLLOW UP TIME: A POPULATION-BASED HEALTHCARE ADMINISTRATIVE STUDY
Bibliographic record
Abstract
INTRODUCTION: The risk of fracture in end-stage kidney disease is at least 4-fold that in general population (GP) and remains higher even after kidney transplantation (KT). However, recent literature reports conflicting results regarding the risk of fracture in KT compared to the GP. We aimed to develop an algorithm for identification of KT patients from healthcare administrative database of Quebec (Canada), and measure the risk of fracture in KT compared to the GP. METHODS: Multiple algorithms combining billing codes related to KT were applied in the Quebec physician claims database to identify all new adult KT patients from 1996 to 2016. Algorithms were validated against the Quebec hospital discharge database. We estimated sensitivity (Sen), specificity (Spe), positive (PPV) and negative predictive value (NPV) to assess the accuracy of each algorithm. KT patients and age and sex matched group (10/case) from the Quebec GP were followed up from their index date to occurrence of first fracture, death, loss to follow-up or 31st of March 2016. Incidence of overall and hip fracture were compared in KT vs the GP using a cox regression model. We estimated Hazard ratios (HR) of fracture and 95% CI according to the follow up time, adjusted for antecedent of fracture, age, sex, diabetes, COPD and social and material deprivation index. Analyses were also stratified according to the date (period) of KT (1996-2001, 2002-2008, 2009-2016). RESULTS: Twelve algorithms were derived. Spe and NPV were 100% for all algorithms. Algorithm 1 (at least one physician claim with a billing code for KT) with excellent Sen (93.05%) and PPV (94.38%) was adopted. incidence rate were 11.42 and 5.44 for 1000 person-years for overall fracture, and 1.29 and 0.48 for hip fracture from 4630 KT and 46300 controls, respectively. Adjusted incidences of overall and hip fracture were significantly higher in KT comparatively to GP for all follow up time. HR of overall fracture was 2.01 (CI: 1.70 to 2.37) at one and 2.04 (CI: 1.47 to 2.82) at seventeen years of follow up. HR for hip fracture progressively increased with follow-up time, estimated at 2.31 (CI: 1.33 to 4.00), 2.53 (CI: 1.75 to 3.64), 2.84 (CI: 1.92 to 4.19), 3.18 (CI: 1.65 to 6.14), and 3.33 (CI: 1.52 to 7.30), at one, five, ten, fifteen, and seventeen years, respectively. The KT period also modified the association between KT and overall or hip fracture risk (Figure I). CONCLUSIONS: Our results demonstrate that algorithms using physician-claims database are accurate and reliable for identifying new cases of KT. Overall and hip fracture risk in KT is higher compared to the GP, while this association changes according to the period KT was carried out.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".