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

2020· article· en· W4242690302 on OpenAlexaboutno aff
Helen Knight, 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)Depression (economics)Activities of daily livingComorbidityPerioperativeCancerPhysical therapyGeriatric Depression ScaleGeriatric oncologyRandomized controlled trialInternal medicineAnxietySurgeryDepressive symptomsPsychiatry

Abstract

fetched live from OpenAlex

e24032 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 older adults with GI cancer planning to undergo surgical resection. We asked patients preoperatively to self-report their physical function (ability to perform activities of daily living [ADLs] and instrumental ADLs [IADLs], higher scores indicate better functioning), QOL (EORTC QLQ-C30, higher scores indicate better QOL), symptom burden (Edmonton Symptom Assessment System [ESAS], higher scores indicate more severe symptoms, scores > 3 considered moderate/severe [mod/sev]), and depression symptoms (Geriatric Depression Scale [GDS], higher scores indicate more severe symptoms, scores > 4 represent a positive screen for depression). We used regression models to identify patient characteristics associated with these PROs. We also explored relationships among PROs and surgical outcomes (receiving planned surgery, postoperative readmissions, and survival). Results: We enrolled 160 of 221 (72.4%) patients approached. A minority of patients were independent in all ADLs (5.2%) and IADLs (47.7%). Patients reported an average of 2.56 mod/sev ESAS symptoms, and 27.7% screened positive for depression, with 53.1% reporting at least one comorbidity. The number of comorbidities was significantly associated with impaired ADLs (B = -0.63, P < .01) and lower QOL (EORTC: B = -2.74, P = .03). For surgical outcomes, patients with better physical function were more likely to receive their planned surgery (ADLs: OR = 1.21, P = .02; IADLS: OR = 1.30, P = .03). Higher QOL correlated with greater odds of receiving planned surgery (EORTC: OR = 1.03, P = .06), but this did not reach statistical significance. A higher number of mod/sev ESAS symptoms was associated with greater postoperative readmission risk within 90 days of surgery (HR = 1.13, P = .03). Better physical function was associated with better postoperative survival (ADLs: HR = 0.87, P = .02; IADLs: HR = 0.73, P < .01), and higher depression scores correlated with worse survival (GDS: HR = 1.13, P = .02). Conclusions: Older adults with GI cancer often have baseline functional limitations and a high physical and psychological symptom burden, all of which are associated with worse surgical 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)0.007
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 score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
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 integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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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