Systematic review and meta-analysis of the correlates of cancer-related fatigue
Bibliographic record
Abstract
Commentary on: Oh HS, Seo WS. Systematic review and meta-analysis of the correlates of cancer-related fatigue. Worldviews Evid Based Nurs 2011;8:191–201.[OpenUrl][1][CrossRef][2][PubMed][3][Web of Science][4] Fatigue is one of the most common problems experienced by patients with cancer, regardless of diagnostic site or treatment modality. Differing from fatigue experienced by the general population, fatigue in cancer is not relieved with rest. It is pervasive and can have a profound effect on quality of life. Generally, patients with cancer develop their own approaches to deal with this challenge, but may not always … [1]: {openurl}?query=rft.stitle%253DWorldviews%2BEvid%2BBased%2BNurs%26rft.aulast%253DOh%26rft.auinit1%253DH.%2BS.%26rft.volume%253D8%26rft.issue%253D4%26rft.spage%253D191%26rft.epage%253D201%26rft.atitle%253DSystematic%2Breview%2Band%2Bmeta-analysis%2Bof%2Bthe%2Bcorrelates%2Bof%2Bcancer-related%2Bfatigue.%26rft_id%253Dinfo%253Adoi%252F10.1111%252Fj.1741-6787.2011.00214.x%26rft_id%253Dinfo%253Apmid%252F21342434%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=10.1111/j.1741-6787.2011.00214.x&link_type=DOI [3]: /lookup/external-ref?access_num=21342434&link_type=MED&atom=%2Febnurs%2F15%2F4%2F108.atom [4]: /lookup/external-ref?access_num=000298260700002&link_type=ISI
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 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 | 0.031 | 0.149 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.018 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".