Bibliographic record
Abstract
OBJECTIVE: To investigate what happens to the serum creatinine (SC) levels of people with initial mild elevations in SC; whether a stable, non-progressive elevation in SC level is the most common scenario; how common a progressive increase in SC is among primary care patients; and how often primary care patients with substantial elevations in SC (>300 micromol/L) progress to end-stage renal disease. DESIGN: Retrospective analysis of laboratory data and chart review. SETTING: Queen's University Family Medicine Centre in Kingston, Ont. PARTICIPANTS: All patients who had SC levels measured at a nearby hospital laboratory between January 1994 and December 1998. MAIN OUTCOME MEASURES: Recently recorded height and weight measurements, latest SC measurements (if available), whether patients had been referred to nephrologists, comorbidity, medications being taken, whether patients were currently undergoing dialysis or had received a renal transplant, and whether patients had died. RESULTS: In the 1434 charts of eligible patients, 64 (4.5%) had elevated initial SC levels (>130 micromol/L) recorded, and 57 of these contained follow-up SC levels also. Among these 57 patients, 32 (56%) saw their SC levels return to normal, including 50% of those whose initial levels had been >300 micromol/L. Only 7 patients (12%) with elevated SC levels progressed to higher levels during the follow-up period. Average age in the study group was 63 years; those with initial elevated SC levels were older than the average (70 years). CONCLUSION: More than half of those with initially elevated SC levels (>130 micromol/L) saw their levels return to normal, including patients whose initial levels had been >300 micromol/L. It seems that a single elevated SC measurement does not predict ongoing decline in renal function.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".