MP639FRAILTY, SURPRISE QUESTION AND MORTALITY IN A HEMODILAYSIS COHORT QUESTION AND MORTALITY IN A HEMODIALYSIS COHORT
Bibliographic record
Abstract
Introduction and Aims: Dialysis patients are characterized by their advanced age , multiple comorbilities and higher mortality than expected in the general population. At the same time, the sensitivity in the Nephrology increases on earlier detection and establishment of palliative care, as integral care in improving quality of life and vital decisions-making. The "Surpise Question" -"Would you be surprised if this patient died in the next 12months?”- has been an useful tool in oncology and palliative-care fields. “Clinical Frailty Scale” ( CFS), developed by the Canadian society of Health and Aging, classifies patients based on disease activity and independence in their daily routines. (1: very fit; 2:well, 3: managing well, 4:vulnerable, 5: mildly frail, 6: moderately frail, 7: severely frail or terminally ill). Both of them have been useful, as well, in detecting patients with poor prognosis and susceptible of specific care. Methods: We have made a prospective study from January 2014 to January 2015. We have recruited 49 chronic patients in our HD unit. Being on dialysis treatment at least 3 months, has been one of inclusion criteria. Medical staff classifies patients in two groups (YES or NO in response to the "Surprise"). We have analyzed their status (alive / dead) in 12months´ time. We have recruited demographic, CKD´s etiologies, time on HD, Charlson Comorbidity Index (CCI), self-rating scales (EUROCOL), Clinical Frailty scale (CFS), analytical and dialysis adequacy variables -as hemoglobine, albumin or Ktv.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".