MétaCan
Menu
Back to cohort

Use of patient-reported outcomes (PROs) to predict treatment outcomes in patients with advanced cancer.

2020· article· en· W3092305301 on OpenAlexaboutno aff
Aparna R. Parikh, Emily E. Van Seventer, Madeleine G. Fish, Kathryn Fosbenner, Katie Kanter, Amirkasra Mojtahed, Jill N. Allen, Lawrence S. Blaszkowsky, Jeffrey W. Clark, Jon S. Du Bois, Joseph W. Franses, Bruce J. Giantonio, Lipika Goyal, Samuel J. Klempner, Eric Roeland, David P. Ryan, Colin D. Weekes, Nora Horick, Ryan B. Corcoran, Ryan David Nipp

Bibliographic record

VenueJournal of Clinical Oncology · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineQuality of life (healthcare)CancerInternal medicinePerformance statusDiseasePhysical therapy

Abstract

fetched live from OpenAlex

186 Background: PROs assessing quality of life (QOL) and physical symptoms often correlate with clinical outcomes in patients (pts) with cancer. Yet, data are lacking about the use of PROs to predict treatment response. We evaluated associations of baseline PROs with treatment response, healthcare use, and survival among pts with advanced gastrointestinal cancer. Methods: We prospectively enrolled pts with metastatic gastrointestinal cancer prior to initiating chemotherapy at Massachusetts General Hospital. At baseline (start of treatment), pts reported their QOL (Functional Assessment of Cancer Therapy General [FACT-G], subscales assess QOL across 4 domains: functional, physical, emotional, social well-being) and symptom burden (Edmonton Symptom Assessment System [ESAS]). Higher scores on FACT-G indicate better QOL, while higher scores on ESAS represent a greater symptom burden. We used regression models to examine associations of baseline PRO scores with treatment response (clinical benefit [CB] or progressive disease [PD] at the time of first scan based on clinical documentation), healthcare use (unplanned hospital admissions), and survival. Results: From 5/2019-3/2020, we enrolled 112 of 131 (85.5% enrollment) consecutive pts (median age = 62.8, 61.6% male, 45.5% pancreatobiliary cancer). For treatment response, 64.3% had CB and 35.7% had PD. Higher ESAS-physical (B = 1.04, p = .027) and lower FACT-G functional (B = 0.92, p = .038) scores at baseline were significant predictors of PD. On the specific ESAS items, pts who experienced PD were more likely to report moderate/severe poor well-being (57.9% vs 29.7%; p = .001), pain (44.7% vs 25.0%; p < .050), drowsiness (42.1% vs 20.3%; p = .024), and diarrhea (23.7% vs 4.7%; p = .008) at baseline. Lower FACT-G total (HR = 0.96, p = .003), FACT-G physical (HR = 0.89, p < .001), FACT-G functional (HR = 0.87, p < .001), and higher ESAS-physical (HR = 1.03, p = .028) scores at baseline were significantly associated with greater risk of hospital admission. Lower FACT-G total (HR = 0.96, p = .009), FACT-G emotional (HR = 0.87, p = .014), as well as higher ESAS-total (HR = 1.03, p = .018) and ESAS-physical (HR = 1.03, p = .040) scores at baseline were significantly associated with greater risk of death. Conclusions: We found that baseline PROs predict treatment response in pts with advanced cancer, namely physical symptoms and functional QOL, in addition to healthcare use and survival outcomes. These findings further support the use of PROs to predict important clinical outcomes, including the novel finding of treatment response.

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 score0.003
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.141
GPT teacher head0.383
Teacher spread0.242 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Clinical OncologySame topicEconomic and Financial Impacts of CancerFrench-language works237,207