Managing hypercalcaemia and hypocalcaemia in cancer patients
Bibliographic record
Abstract
PURPOSE OF REVIEW: Hypercalcaemia is commonly associated with cancer which is usually advanced and portends a poor prognosis. Hypocalcaemia is more often seen as a complication of therapy aimed at reducing skeletal morbidity rather than an effect of the cancer itself. We present an overview of calcium disorders in malignant disease. RECENT FINDINGS: A significant proportion of patients who have a cancer and become hypercalcaemic have an alternative cause for their hypercalcaemia.Evidence for the use of loop diuretics is lacking, and such agents should not be routinely used unless significant volume overload occurs during rehydration. Bisphosphonates are generally established as first-line therapy after volume expansion with saline. As knowledge of bone biology increases, there is interest in the use of the mAb denosumab, for the management refractory hypercalcaemia. Knowledge of the vitamin D status, and supplementation of vitamin D, may reduce the risk of hypocalcaemia when potent antiresorptive medications are being used. SUMMARY: Calcium disorders can be predicted in many tumour types and with antiresorptive therapy. A logical approach to prevention and management of these imbalances should be incorporated into cancer patient care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".