MP628PREDICTING DEATH ON MAINTENANCE HEMODIALYSIS- A COMPLEX TASK IN PREVALENT ELDERS
Bibliographic record
Abstract
Introduction and Aims: Information on predicted outcomes including mortality helps older individuals select treatment options for end stage disease renal disease. A prognostic model incorporating the surprise question (SQ) “Would I be surprised if this patient died within the next 6 months?” and parameters including patient age, serum albumin, dementia and peripheral vascular disease( PVD) has been validated for patients undergoing hemodialysis for the prediction of 6 month survival. We conducted a prospective observational study in patients aged ≥75 years undergoing maintenance hemodialysis to test the association of mortality at 6 months of recruitment with the percentage chance of survival predicted by the model. Methods: Patients were recruited from 3 tertiary care hospitals and associated stand alone dialysis centres in Kerala, South India as part of a cross sectional study of frailty in older hemodialysis patients.SQ was answered by the principal investigator for all patients. The Montreal Cognitive Assessment Instrument (MOCA) score ≤10 was used to define dementia. Patient records were reviewed to ascertain age, serum albumin and the presence of PVD as well as coronary artery disease (CAD) and cerebrovascular accident (CVA). Predicted 6 month percentage chance of survival was generated using the model to categorise patients into 3, category 1- <60%, category 2- 60- 79% and category 3- >80%. Frailty was documented by scoring on a five domain (weight loss, exhaustion, low physical activity, weak grip and slow walking) tool. The number of deaths in the six months from recruitment was documented and causes were verified from records.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".