Systematic reviews and meta-analyses in geriatric oncology.
Bibliographic record
Abstract
e21517 Background: Cancer incidence for most malignancies increases with age with the majority diagnosed after age 65. Aging is also associated with an increasing number of major comorbidities and greater risk and consequences of treatment-related complications. Geriatric Oncology has emerged as a subdiscipline within oncology focused on clinical management and research related to the elderly. Methods: A comprehensive search of the English language literature between 1990-2016 was undertaken for systematic reviews or meta-analyses (SRMAs) related to geriatric oncology. Titles, abstracts and full text manuscripts when needed were reviewed. 1,088 potentially eligible records were identified including 703 not limited to elderly patients, 89 not cancer studies and 236 not SRMAs. Results:More than half of 61 eligible studies were published in the last five years including systematic reviews in 42 (69%), meta-analyses in 42 (69%) and both in 23 (40%). Studies came from Europe (30), US (14), Canada (9), Asia (7) and South America (1) with elderly age cutoffs ranging from > 60 to > 80. While 17 reviews included multiple cancer types, 44 were limited to lung cancer (9), colorectal cancer (8), breast cancer (7), multiple myeloma (5) and lymphoma (4). Research focus was survivorship or end-of-life (41), treatment (24), geriatric assessment (11) and supportive care (8). Studies were limited to randomized controlled trials (37), non-RCTs (9) and both (16). The primary outcome was overall survival (39), progression free or relapse-free survival (15), response or recurrence (11), treatment-related toxicity (21) and geriatric assessment or frailty (9). More than half of SRMAs included < 10 studies while 20% included > 30 with the number of subjects in included trials ranging from 153 to > 15,000. Conclusions: The development of Geriatric Oncology has spanned nearly three decades. While a strong evidence base of published research including rigorous SRMAs in Geriatric Oncology has only emerged over the past decade, steady growth across a range of topics and outcomes relevant to cancer in the elderly is apparent.
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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 | 0.046 | 0.150 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.032 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".