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Record W4293282442 · doi:10.1016/s2666-7568(22)00169-6

Integrated Geriatric Assessment and Treatment Effectiveness (INTEGERATE) in older people with cancer starting systemic anticancer treatment in Australia: a multicentre, open-label, randomised controlled trial

2022· article· en· W4293282442 on OpenAlexfundno aff
Wee Kheng Soo, Madeleine King, A. L. Pope, Phillip Parente, Pēteris Dārziņš, Ian D. Davis

Bibliographic record

VenueThe Lancet Healthy Longevity · 2022
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsnot available
FundersNational Health and Medical Research CouncilMedical Research CouncilMonash UniversityEastern Health
KeywordsMedicineOpen labelCancerRandomized controlled trialIntensive care medicineInternal medicineOncology

Abstract

fetched live from OpenAlex

BACKGROUND: The effectiveness of comprehensive geriatric assessment (CGA) in improving health outcomes in cancer settings is unclear. We evaluated whether CGA can improve health-related quality of life (HRQOL) in older people with cancer who are starting systemic anticancer treatment. METHODS: INTEGERATE is a multicentre, open-label, pragmatic, parallel-group, randomised controlled trial that was done at three hospitals in Australia. Participants aged 70 years and older with solid cancer or diffuse large B-cell lymphoma planned for chemotherapy, targeted therapy, or immunotherapy, were randomly assigned (1:1; using a central computer-generated minimisation algorithm with a random element, balancing treatment intent, cancer type, age, sex, and performance status) to receive CGA integrated into oncology care (integrated oncogeriatric care) or usual care only. Group assignment was not concealed from the participants and clinicians. The primary outcome was HRQOL over 24 weeks, assessed at baseline, week 12, week 18, and week 24, using the Elderly Functional Index (ELFI; score range 0-100). Analyses were by intention to treat. The trial is registered with ANZCTR.org.au, ACTRN12614000399695, and is completed. FINDINGS: Between Aug 18, 2014, and Sept 5, 2018, 154 participants were randomly assigned to integrated oncogeriatric care (n=76) or usual care (n=78). 13 participants died by week 12 and 130 (92%) of the remaining 141 participants completed two or more ELFI assessments. Participants assigned to integrated oncogeriatric care reported better adjusted ELFI change scores over 24 weeks compared with those in the usual care group (overall main effect of group: t=2·1, df=213, p=0·039; effect size=0·38), with maximal between-group differences at week 18 (mean difference in change 9·8 [95% CI 2·4-17·2]; p=0·010, corrected p=0·030, effect size=0·48). The integrated oncogeriatric care group also had significantly fewer unplanned hospital admissions at 24 weeks (multivariable-adjusted incidence rate ratio 0·60 [95% CI 0·42-0·87]; p=0·0066). No statistically significant between-group difference was observed in overall survival. INTERPRETATION: CGA led to better quality of life and health-care delivery in older people receiving systemic anticancer treatment. Routine CGA-based interventions should be considered in at-risk older people starting systemic anticancer treatment. FUNDING: National Health and Medical Research Council (Australia), Monash University, and Eastern Health.

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 imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.015
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.063
GPT teacher head0.392
Teacher spread0.329 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations211
Published2022
Admission routes1
Has abstractyes

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