Physical compatibility of calcium gluconate and magnesium sulfate injections
Bibliographic record
Abstract
Oxaliplatin, a potent alkylating agent, is widely used for the treatment of gastrointestinal cancers despite its associated adverse effects including sensorial peripheral neuropathy (SPN).1 Oxalate ions are toxic metabolites of oxaliplatin. They appear to be the root cause of oxaliplatin-related SPN by chelating physiologically relevant divalent ions. A proposed antidote to oxaliplatin-related SPN is the co-administration of calcium gluconate and magnesium sulfate solutions.2 Several clinical studies have discussed the simultaneous i.v. administration of 1 g of each salt in an often nonspecified vehicle.3,4 Considering the low solubility of calcium sulfate, the precipitation of this salt is a serious concern when calcium gluconate and magnesium sulfate are mixed. We conducted an in vitro study to evaluate the compatibility of admixtures of calcium gluconate and magnesium sulfate in polyvinyl chloride (PVC) bags of 0.9% sodium chloride injection and 5% dextrose injection. Samples were aseptically prepared by adding 10 mL of 10% calcium gluconate injectiona and 2 mL of 50% magnesium sulfate injectionb to 100- and 250-mL PVC bags containing 5% dextrose injectionc,d and 0.9% sodium chloride injectione,f using polypropylene syringes.g All containers were stored protected from light at 24.5–25.5 °C and 4.5–5.5 °C.h
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".