Using linked administrative data to study periprocedural mortality in obesity and chronic kidney disease (CKD)
Bibliographic record
Abstract
Both obesity and chronic kidney disease (CKD) are associated with adverse periprocedural outcomes, but it is unknown how these two common conditions interact to influence risk. We examined the feasibility of combining a new procedure-related, obesity-specific flag with administrative and laboratory data and assessed the joint association between obesity and CKD with mortality. Since 2007, Alberta physicians may claim a fee supplement for performing eligible surgical and non-surgical procedures on patients with documented BMI ≥ 35 kg/m(2). We linked this information to the Alberta Kidney Disease Network registry. Participants were classified into four mutually exclusive groups based on the presence/absence of both obesity (BMI ≥ 35 kg/m(2)) and CKD (eGFR < 60 mL/min/1.73 m(2)). Mortality was assessed at 30 days following the index procedure. Of 393 659 participants, 9% were obese. Overall, 8% had obesity only, 78% neither obesity nor CKD, 13% CKD only and 1% both obesity and CKD. Unadjusted risks of mortality at 30 days were 0.3, 0.4, 2.0 and 2.1%, respectively--but decreased to 0.1, 0.2, 0.3 and 0.3%, respectively, after adjustment for age, sex, socioeconomic status, procedure type and other comorbidities. Administrative data can be feasibly combined with disease registries to study obesity-related outcomes. Results from the linked dataset demonstrated face validity--subjects with both obesity and CKD were at increased risk of periprocedural mortality, and this was driven in part by differences in age and comorbidity.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".