People receiving dialysis in the morning have better subjective sleep quality than those who receive dialysis at other times
Bibliographic record
Abstract
Commentary on: Wang MY, Chan SF, Chang LI, et al. Better sleep quality in chronic haemodialyzed patients is associated with morning-shift dialysis: a cross-sectional observational study. Int J Nurs Stud 2013;50:1468–73.[OpenUrl][1][CrossRef][2][PubMed][3] Sleep disturbances are a common problem among haemodialysis patients, with an estimated prevalence of 50% to 80%.1 The implications of disturbed sleep are substantial; compared with haemodialysis patients who do not report sleep disturbances, poor sleep is independently associated with lower health-related quality of life and an increased relative risk in mortality of 16%.2 As … [1]: {openurl}?query=rft.jtitle%253DInt%2BJ%2BNurs%2BStud%26rft.volume%253D50%26rft.spage%253D1468%26rft_id%253Dinfo%253Adoi%252F10.1016%252Fj.ijnurstu.2013.02.010%26rft_id%253Dinfo%253Apmid%252F23499167%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/external-ref?access_num=10.1016/j.ijnurstu.2013.02.010&link_type=DOI [3]: /lookup/external-ref?access_num=23499167&link_type=MED&atom=%2Febnurs%2F17%2F4%2F108.atom
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.004 |
| 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; both teacher heads agree on what is shown here.
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".