SHOULD CALCIUM SUPPLEMENTS BE PRESCRIBED PROPHYLACTICALLY FOR PATIENTS DIAGNOSED WITH A HIGH-GRADE GLIOMA (HGG) ON PROLONGED STEROID USE?
Bibliographic record
Abstract
Abstract AIMS The case study review will evaluate whether patients on long term steroid would benefit from calcium supplements and if a protocol for a bone density scan should be in place in relation to how long patients have been on steroids for. Duration of steroid use increases patients’ risk to steroid related toxicity when taken more than 3 weeks. These can include endocrine, skeletal and psychiatric side effects amongst many others. It is therefore important to assess how we can improve patients’ quality of life (QOL) with limited prognosis. METHOD In a single centre, patients diagnosed with a HGG will be reviewed if they were diagnosed with osteoporosis or spinal fractures between 2020- 2022, subsequent from prolonged steroid use. RESULTS All 7 patients identified had presented with severe lower back pain. 4 patients initially had bone density scans which prompted an MRI due to low minerals for patient age and osteoporosis, whilst the remaining 3 patients only had an MRI scan. All 7 patients had spinal fractures. The average dexamethasone use was 9 months unable to wean to stop. CONCLUSIONS Patients diagnosed with a HGG tumour sadly continue to have poor prognosis. For patients who have had limited debulking or biopsy only, this leaves them prone to require ongoing steroid use to help with side effects to improve QOL. Though the figures are small, we can see patients who struggled to wean to stop dexamethasone since their intracranial surgery have had their QOL impacted due to steroid toxicities with no previous related comorbidities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".