Screening for renal disease using serum creatinine: who are we missing?
Bibliographic record
Abstract
BACKGROUND: Appropriate management and timely referral of patients with early renal disease often depend on the identification of renal insufficiency by primary care physicians. Serum creatinine (SCr) levels are frequently used as a screening test for renal dysfunction; however, patients can have significantly decreased glomerular filtration rates (GFR) with normal range SCr values, making the recognition of renal dysfunction more difficult. This study was designed to estimate the prevalence of patients who have significantly reduced GFR as calculated by the Cockcroft-Gault (C-G) formula, but normal-range SCR: METHODS: The study included 2781 outpatients referred by community physicians to an urban laboratory network for SCr measurement. GFR was estimated using the C-G formula. Patients were grouped according to the concordance of SCr level abnormalities (abnormal >130 micromol/l) with significantly abnormal C-G values (abnormal </=50 ml/min). The C-G value of < or =50 ml/min was chosen to reflect substantial renal impairment in all age groups. A further analysis of historical laboratory data was undertaken to determine if there were previously documented changes in renal function parameters in those patients who had overt renal dysfunction during the study period. RESULTS: Of the 2781 outpatients referred, 2543 (91.4%) had normal SCr levels. Of these patients, 387/2543 (15.2%) had C-G calculated GFR < or =50 ml/min, representing substantially impaired renal function. Among patients with normal SCr, abnormal C-G values were identified in 47.3% > or =70 years old, 12.6% 60-69 years old, and 1.2% 40-59 years old. Analysis of historical available laboratory data for patients with abnormal SCr and abnormal C-G values showed that 2 years prior to the study period, 72% of this group had abnormal SCr, while 18% had normal SCr with abnormal C-G values, and 10% had normal SCr with normal C-G values. CONCLUSIONS: This study documents the substantial prevalence of significantly abnormal renal function among patients identified by laboratories as having normal-range SCR: Including calculated estimates of GFR in routine laboratory reporting may help to facilitate the early identification of patients with renal impairment.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".