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Record W3009190269 · doi:10.6004/jnccn.2019.7355

Screening Tool Identifies Older Adults With Cancer at Risk for Poor Outcomes

2020· article· en· W3009190269 on OpenAlex
Ryan David Nipp, Leah L. Thompson, Brandon Temel, Charn‐Xin Fuh, Christine Server, Paul Kay, Sophia Landay, Daniel E. Lage, Lara Traeger, Erin Scott, Vicki A. Jackson, Nora Horick, Joseph A. Greer, Areej El‐Jawahri, Jennifer S. Temel

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of the National Comprehensive Cancer Network · 2020
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsnot available
FundersNational Cancer Institute
KeywordsMedicineQuality of life (healthcare)Depression (economics)Internal medicineCancer

Abstract

fetched live from OpenAlex

BACKGROUND: Oncologists often struggle with managing the complex issues unique to older adults with cancer, and research is needed to identify patients at risk for poor outcomes. METHODS: This study enrolled patients aged ≥70 years within 8 weeks of a diagnosis of incurable gastrointestinal cancer. Patient-reported surveys were used to assess vulnerability (Vulnerable Elders Survey [scores ≥3 indicate a positive screen for vulnerability]), quality of life (QoL; EORTC Quality of Life of Cancer Patients questionnaire [higher scores indicate better QoL]), and symptoms (Edmonton Symptom Assessment System [ESAS; higher scores indicate greater symptom burden] and Geriatric Depression Scale [higher scores indicate greater depression symptoms]). Unplanned hospital visits within 90 days of enrollment and overall survival were evaluated. We used regression models to examine associations among vulnerability, QoL, symptom burden, hospitalizations, and overall survival. RESULTS: Of 132 patients approached, 102 (77.3%) were enrolled (mean [M] ± SD age, 77.25 ± 5.75 years). Nearly half (45.1%) screened positive for vulnerability, and these patients were older (M, 79.45 vs 75.44 years; P=.001) and had more comorbid conditions (M, 2.13 vs 1.34; P=.017) compared with nonvulnerable patients. Vulnerable patients reported worse QoL across all domains (global QoL: M, 53.26 vs 66.82; P=.041; physical QoL: M, 58.95 vs 88.24; P<.001; role QoL: M, 53.99 vs 82.12; P=.001; emotional QoL: M, 73.19 vs 85.76; P=.007; cognitive QoL: M, 79.35 vs 92.73; P=.011; social QoL: M, 59.42 vs 82.42; P<.001), higher symptom burden (ESAS total: M, 31.05 vs 15.00; P<.001), and worse depression score (M, 4.74 vs 2.25; P<.001). Vulnerable patients had a higher risk of unplanned hospitalizations (hazard ratio, 2.38; 95% CI, 1.08-5.27; P=.032) and worse overall survival (hazard ratio, 2.26; 95% CI, 1.14-4.48; P=.020). CONCLUSIONS: Older adults with cancer who screen positive as vulnerable experience a higher symptom burden, greater healthcare use, and worse survival. Screening tools to identify vulnerable patients should be integrated into practice to guide clinical care.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.610
Threshold uncertainty score0.540

Codex and Gemma teacher scores by category

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

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 head0.045
GPT teacher head0.319
Teacher spread0.274 · 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