MétaCan
Menu
← Back to cohort

The FARGO study: Frailty assessment for risk prediction in gynecologic oncology.

2023· article· en· W4379340597 on OpenAlexaff
Julie My Van Nguyen, Danielle Vicus, Liat Hogen, Tiffany Zigras, Yetiani M. Roldán-Benítez, Maura Marcucci

Bibliographic record

VenueJournal of Clinical Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsTrillium Health CentrePrincess Margaret Cancer CentreHamilton Health SciencesUniversity Health NetworkCredit Valley HospitalHealth Sciences CentreSinai Health SystemSunnybrook Health Science CentreMcMaster University
Fundersnot available
KeywordsMedicineGynecologic oncologyPerioperativeBiomarkerInternal medicinePopulationMalignancyRisk assessmentOncologyIntensive care medicineSurgery

Abstract

fetched live from OpenAlex

TPS12137 Background: Little is known about how to best predict postoperative outcomes and chemotherapy tolerance in an increasingly aging and complex gynecologic oncology (GO) population. Frailty is a multidimensional age-related and disease-related state of vulnerability to stressors due to reduced capacity of different physiological systems. Resource limitations are a barrier to routine adoption of frailty assessments through comprehensive clinical evaluations in daily practice. This highlights the relevance of studying potential biomarkers of frailty, which could complement or replace clinical assessments. Frailty has been associated with worse perioperative and oncologic outcomes in many subspecialties, however the literature in patients with gynecologic malignancies is very limited. Methods: The FARGO study is a multi-centre prospective cohort study of 280 patients aged ≥55 years-old undergoing surgery, with or without chemotherapy for a suspected or confirmed gynecologic malignancy. Patient recruitment will occur over 13 months. The over-arching goal is to evaluate the predictive value of frailty assessments in predicting surgical and chemotherapy outcomes. The two primary objectives are: 1) To evaluate the predictive value of preoperative frailty assessment based on the Frailty Phenotype (FP), compared to a perioperative cardiovascular risk assessment, in predicting the composite of all-cause death or new disability at 6 months after surgery 2) To create of a biobank to explore possible biomarkers of frailty, including mitochondrial DNA levels. Secondary objectives include to evaluate the predictive value of clinical and biomarker frailty assessment for 30-day postoperative complications, for chemotherapy tolerance and 1-year recurrence-free survival. Patients will undergo frailty and cardiovascular risk assessment prior to surgery and/or neoadjuvant chemotherapy. A trained clinical research assistant will measure frailty based on the clinical frailty scale (CFS) and the FP. The cardiovascular risk calculation will be based on the preoperative Revised Cardiac Risk Index, age, and occurrence of myocardial injury after non-cardiac surgery (MINS). Research personnel blinded to the frailty and cardiovascular risk assessment will evaluate study outcomes including death, disability, and clinical events at 30 days, 6 months, and 12 months after surgery. Disability will be measured with the WHODAS 2.0 (World Health Organization Disability Assessment Schedule), a validated patient-reported outcome measure. Pre-specified blood biomarkers will be measured. Our study emphasizes patient-centered outcomes (i.e. loss of independence, short and long-term disability) and system-level outcomes. Our findings may have important implications to assist in risk stratification and planning for oncologic treatments and perioperative care needs. Clinical trial information: will be added later .

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)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.222
GPT teacher head0.548
Teacher spread0.326 · 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 designObservational
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

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Clinical Oncology→Same topicFrailty in Older Adults→French-language works237,207→