Using Administrative Datasets to Study Outcomes in Dialysis Patients
Bibliographic record
Abstract
BACKGROUND: The use of administrative health data and other secondary data sources to conduct research are increasing, and the quality of these data requires careful scrutiny to ensure that findings of studies based on them are accurate. METHODS: We conducted a multicenter, chart-abstraction study in Ontario, Canada to evaluate the ability of linked administrative databases to identify important baseline demographic and treatment information, changes in dialysis treatment modality over time, and the occurrence of important outcome events in incident dialysis patients. The medical record was considered the reference standard. RESULTS: Within administrative databases, demographic information was very well coded, as was the location where individuals started dialysis, the first treatment modality, the first outpatient modality, and the treatment that was in use 90 days after the start of therapy. The ability to accurately recreate an individual patient's entire dialysis treatment history using physician billing claims was somewhat limited. The treatment changes were often identified in the correct temporal sequence, but the dates that the events occurred did not agree well. Finally, important outcomes including the death and kidney transplantation were captured well, although the recovery of kidney function could not be evaluated because of poor inter-rater reliability. CONCLUSIONS: This validation study provides important information concerning the ability to detect variables related to dialysis care using administrative datasets. Validation work should focus not only on the ability of secondary data to identify baseline comorbidities, but should also attempt to verify that other key variables required to conduct analyses are reliably captured.
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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 | 0.042 | 0.148 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| 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".