Prevalence of hypercalcemia among cancer patients in the United States
Bibliographic record
Abstract
Hypercalcemia of malignancy (HCM) is a serious metabolic complication whose population-based prevalence has not been quantified. Rates of HCM differ by tumor type, with highest rates reported in multiple myeloma and lowest among colorectal and prostate cancer patients. This analysis estimates HCM prevalence in the US. This retrospective study used the Oncology Services Comprehensive Electronic Records (OSCER) warehouse of electronic health records (EHR) including laboratory values from 569000 patients treated at 565 oncology outpatient sites. OSCER data were projected to the national level by linking EHR to claims data. Cancer patients included were ≥18 years, and had serum calcium (Ca) and albumin (for corrected serum Ca [CSC]) records. Period prevalence was estimated by HCM CTCAE grade, tumor type, and year (2009-2013). Estimates were adjusted to capture patients diagnosed with HCM outside oncology practices based on a subset of patients linkable to office and hospital data. The analysis included 68023 (2009) to 121482 (2013) cancer patients. In 2013, patients with HCM had a median of six Ca tests, 69.7% had chemotherapy, and 34% received bone modifying agents. HCM rates were highest for multiple myeloma patients (7.5% [2012]-10.2% [2010]), lowest for prostate cancer (1.4% [2012]-2.1% [2011]).The estimated adjusted annual prevalence of HCM from 2009 to 2013 was 95441, 96281, 89797, 70158, and 71744, respectively. HCM affected 2.0-2.8% of all cancer patients. EHR data from oncology clinics were critical for this study because these data contain results from laboratory studies (i.e., serum calcium values) that are routinely ordered in that setting. We estimated that the prevalence of HCM in the US in 2013 is 71744, affecting approximately 2% of cancer patients overall. This percentage differs by tumor type and appears to have decreased over the five-year study period.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.001 | 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".