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Patient-reported outcomes (PROs) in older adults with gastrointestinal (GI) cancer undergoing surgery.

2020· article· en· W3028866718 on OpenAlexaboutno aff
Helen Knight, Zhi Ven Fong, Carolyn L. Qian, Emilia Kaslow-Zieve, Chinenye C. Azoba, Cristina R. Ferrone, Hiroko Kunitake, Carlos Fernández‐del Castillo, Michael Lanuti, Motaz Qadan, Rocco Ricciardi, Keith D. Lillemoe, Esteban Franco‐Garcia, Terrence A. O’Malley, Vicki A. Jackson, Joseph A. Greer, Areej El‐Jawahri, Jennifer S. Temel, Ryan David Nipp

Bibliographic record

VenueJournal of Clinical Oncology · 2020
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineQuality of life (healthcare)Activities of daily livingDepression (economics)PerioperativeComorbidityGeriatric oncologyCancerPhysical therapyGeriatric Depression ScaleInternal medicineSurgeryAnxietyDepressive symptomsPsychiatry

Abstract

fetched live from OpenAlex

159 Background: Older adults with GI cancer often experience poor surgical outcomes, yet little is known about their PROs, such as physical function, quality of life (QOL), and physical and psychological symptom burden. Methods: As part of a randomized trial of perioperative geriatric care, we prospectively enrolled adults age ≥65 with GI cancer planning to undergo surgical resection. We asked patients preoperatively to self-report their physical function (activities of daily living [ADLs] and instrumental ADLs [IADLs]), QOL (EORTC QLQ-C30), symptom burden (Edmonton Symptom Assessment System [ESAS], scores > 3 considered moderate/severe [mod/sev]), depression symptoms (Geriatric Depression Scale [GDS], scores > 4 represent a positive screen for depression), and comorbidities. We used regression models to explore relationships among PROs and clinical outcomes (receiving planned surgery, postoperative complications [Clavien-Dindo], hospital readmissions within 90 days, and survival). Results: From 9/2016 - 4/2019, we enrolled 160 of 221 (72.4%) patients approached (median age: 72, range: 65-92). At baseline, most (53.1%) reported at least one comorbidity and required assistance with ADLs (94.8%) and IADLs (52.3%). Patients reported an average of 2.56 mod/sev ESAS symptoms, and 27.7% screened positive for depression. For surgical outcomes, 137 patients (85.6%) underwent planned surgery, and 99 (72.2%) of these had at least one postoperative complication. Greater independence with ADLs was associated with undergoing planned surgery (OR = 1.21, P = .02), lower risk of complications (OR = 0.81, P < .01), and improved survival (HR = 0.87, P = .02), but not readmissions. Greater independence with IADLs was associated with undergoing planned surgery (OR = 1.30, P = .03) and improved survival (HR = 0.73, P < .01), but not other outcomes. Higher baseline QOL was only associated with lower risk of postoperative complications (OR = 0.97, P = .04). Higher depression scores were only associated with worse survival (HR = 1.13, P = .02). Higher baseline symptom burden predicted for shorter time to readmission (HR = 1.13, p = .03). Patient-reported number of comorbidities was associated with shorter time to readmission (HR = 1.49, p = .03) and higher risk of complications (OR = 1.70, P = .03). Conclusions: Older adults with GI cancer often have baseline functional limitations and a high symptom burden, all of which are associated with worse clinical outcomes. Future work should study whether addressing preoperative PROs could improve older patients’ surgical outcomes. Clinical trial information: NCT02810652 .

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)0.005
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
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.098
GPT teacher head0.417
Teacher spread0.320 · 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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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