Analysis of patients with hypomagnesemia using the Japanese Adverse Drug Event Report database (JADER)
Bibliographic record
Abstract
PURPOSE: In order to clarify the occurrence of hypomagnesemia in Japan, we conducted a database search and analysis using the Japanese Adverse Drug Event Report database (JADER). METHODS: Among the cases recorded in JADER between April 2004 and December 2015, we targeted "hypomagnesemia" and analyzed the patients' backgrounds, drug involvement, other adverse events reported with hypomagnesemia, the time of hypomagnesemia onset, outcomes, and year when reported. For drugs with three or more reports, the signal index was calculated using the Reporting Odds Ratio (ROR) method. In addition, the association between hypomagnesemia onset and other adverse events was investigated using association analysis. RESULTS: The total number of reported hypomagnesemia cases was 201. Males accounted for 62.7%, and patients in their sixties formed a large peak. Three or more cases were reported for 23 causative drugs, among which anti-EGFR antibody, calcineurin inhibitor, platinum antitumor agent and proton pump inhibitor accounted for the majority. ROR analysis detected signals for 18 drugs, and an association was found between hypomagnesemia and other electrolyte abnormalities for those drugs. The median time until onset of hypomagnesemia was classified into three patterns: around 10 days, around 30 days, and longer. Analysis of the report year revealed an increasing tendency in recent years, although increases/decreases were evident depending on fiscal years. CONCLUSION: Our survey was able to reveal the factors associated with the occurrence of hypomagnesemia.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".