Sex, Gender, and Quality of Life in Hemodialysis
Bibliographic record
Abstract
Background: Women on conventional hemodialysis (HD) have a lower reported quality of life (QoL) compared to men. Incremental HD, which gradually increases dialysis dose over time, is a potential strategy to improve QoL. Despite differences in QoL, current HD prescription remain sex (biology) and gender (sociocultural) blind. We aimed to determine if sex and gender-related measures (gender roles, relations and identity) were associated with QoL when initiating incremental HD (<3 sessions/week) compared to conventional HD (3 sessions/week). Methods: Patients initiating HD in Alberta, Canada, were invited to enrol (June-December 2021) in this prospective cohort study. Sex assigned at birth was obtained by self-report. Eligibility for incremental HD at initiation is determined by kidney care providers using standardized assessments. The Kidney Disease Quality of Life 36 (KDQOL-36) and the GENESIS-PRAXY Gender Questionnaires were administered at baseline and at 3-months. The physical component score (PCS) and mental component scores (MCS) of the KDQOL-36 determined QoL. The GENESIS-PRAXY creates a composite gender score that is measured on a spectrum, with lower scores consistent with behaviours ascribed to males and high scores consistent behaviours ascribed to females. Non-parametric tests analysed the association between sex and QoL by HD type. Multiple linear regressions explored the association between gender score and QoL by HD type. Results: All 48 participants identified as cisgender. There were 22 participants on conventional HD (7 female, 15 male) and 26 on incremental (13 female, 13 male) (p=0.11). There was no significant change in MCS (female p= 0.68, male: p=0.41) or PCS (female p= 0.84, male: p=0.61) after 3-months regardless of HD type. Linear regression analysis showed a significant negative association between gender score and PCS (p<0.01, R2=0.54) but not with MCS (p=0.09, R2=0.43). Conclusions: Sex was not associated with QoL in this cisgender cohort. Behaviours traditionally ascribed to females as indicated by higher gender scores were associated with decreasing QoL with regards to physical health. Understanding sex and gender differences will allow care providers to better address the needs of patients receiving HD.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".