Bibliographic record
Abstract
Purpose: To analyze all patients who died while on hemodialysis from a single institution 1995–2004 and determine factors that influence duration on HD (months). Material and Methods: Review of the EMR from the dialysis unit for demographics, treatment parameters, cause of death, laboratory data. Two main groups those who stop dialysis (SHD) vs non stop dialysis (NSHD) data as mean and SD. Observations done at the start/mid/end of the period on dialysis by Kaplan Meier analysis. Results: 348 deaths, reported SMR for 2000–93(0.36,0.48,0.72,0.79), average comorbidity index 7.1–16.4; 49.1% male; age 67.6(12.3); 58.2% DM, 117(33.6%) stop dialysis. Detail analysis from 2000–2004; 187 deaths; 33.6% catheter, 41.1% AV graft at the time of death; 26% on no BP meds at the end, 64% 1–2 BP meds at the end. Cause Stop No Stop CV 33(28%) 145(62.7%) Infection 10(8.5%) 41(17.7%) Other 74(63.5%) 45(19.6%) Most SHD deaths due to uremia (60%), Cancer 17%, occurred 36% CVA. SHD vs NSHD: older 70.8 (12.9) vs 65.9(12.9) p < 0.001. No difference in average months in dialysis 41.2 (38.9) vs 48.7(47.7)NS, in URR (start/end), UF during Hd, predialysis MAP (start/end: 105(15) 196(15)/95 (20) , 96(20)) albumin, phosphorous. Lower creatinine (at the end 6.2(1.5) 7.4(2.4)) higher Kt/V at end 1.47(0.4) vs 1.29(0.3). BMI lower at star/mid/end of the observation period. Conclusions: A large proportion of deaths were due to discontinuation of dialysis, older patient discontinue dialysis more frequently, the length of time on dialysis (months) is related more to nutritional factors (creatinine, BMI)
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".