Exploring the intersection of cancer, sepsis, and frailty: a scoping review
Bibliographic record
Abstract
To explore the intersection of cancer, sepsis, and frailty, and consider potentials for rehabilitation interventions to manage these conditions. We conducted a scoping review guided by the Joanna Briggs Institute’s framework. We searched seven databases for studies that included: (1) adults > 18 years of age; (2) with a current or past diagnosis of cancer; (3) a current or past diagnosis of sepsis; (4) explicitly identified frailty as an inclusion criterion; (5) explored the intersection of these condition; and (6) were published in English. We screened titles/abstracts, reviewed full texts, and performed data extraction in duplicate. A qualitative synthesis summarized findings related to the "population, context, concept” framework. Of 2083 citations, we included 18 studies, which included 3,206,672 participants. Most (61%) were retrospective cohort studies of acute hospital datasets collected in the United States. Fourteen (78%) of the studies explicitly defined frailty, however there was inconsistency in frailty measurement, with 12 unique tools described. Only two studies (11%) provided criteria for diagnosing sepsis. Of 17 studies exploring the effect of frailty on risk of sepsis in those with cancer, 10 (59%) found a statistically significant association between increased frailty and number of poor outcomes. It appears frailty is associated with an increased risk of sepsis in those with cancer. Sepsis is not well reported on in the literature and future research standardizing measures of frailty would help further evaluating the association of these conditions. It remains uncertain whether rehabilitation strategies could maximize function in this population.
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 | 0.034 | 0.143 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.030 | 0.028 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".