Bibliographic record
Abstract
BACKGROUND: We previously reported a very high incidence of calciphylaxis, mainly in peritoneal dialysis (PD) patients. Although we identified several risk factors for the condition, including PD, we were unable to identify why our particular unit had such a high frequency of the condition and a reliable treatment. OBJECTIVES: To assess the apparent changing frequency of the condition and the response to therapy, and to attempt to determine putative factors that might explain our uniquely high incidence of calciphylaxis. METHODS: A prospective clinical record was kept on all patients that developed calciphylaxis in our center [both PD and hemodialysis (HD) units] between 1998 and 2006. RESULTS: Of the 59 patients that developed calciphylaxis, 54 were on PD, 4 were on HD, and 1 was in predialysis. In the PD population, the mean yearly incidence from 1998 to 2003 was 4.5/100 patient-years, falling to 1.3/100 patient-years in 2004-2006. The percent of patients not taking calcium salts fell during this time period. Conversion to HD led to marked early improvement. A marked discrepancy between the levels of ionized calcium (routinely used in our center) and corrected serum calcium was found, with most cases of hypercalcemia (corrected) being missed by using ionized values. CONCLUSIONS: The incidence of calciphylaxis is falling dramatically. This may be related partially to reduction in usage of calcium salts. Conversion to HD is beneficial. Our uniquely high incidence of calciphylaxis may be related to our use of ionized calcium levels to monitor these patients.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".