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Validation of the patient-generated part of the PG-SGA against the malnutrition screening tool (MST).

2012· article· en· W2526991509 on OpenAlexaff
Nelda Swinton, Goulnar Kasymjanova, Mary Grossman, Victor Cohen, Carmela Pepe, Jason Agulnik, David Small

Bibliographic record

VenueJournal of Clinical Oncology · 2012
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsMedicineMalnutritionCancerLung cancerInternal medicineQuality of life (healthcare)PediatricsNursing

Abstract

fetched live from OpenAlex

e19626 Background: It is important to identify cancer patients (pts) who are at risk for malnutrition as many pts experience an array of nutritional problems which can impact upon their treatment plan and quality of life. This requires using an easy and reliable screening tool such as the MST or the scored PG-SGA. The scored PG-SGA is a more extensive tool consisting of a patient (pt) - generated section identifying symptoms that can impact on nutritional status. It also includes a second part consisting a pt’s physical examination. Both sections are summed to give an overall PG-SGA score. We hypothesized that the PG-SGA‘s pt-generated part alone, could be a comparable screening tool to the MST. Methods: We performed a prospective analysis from our out-patient pulmonary oncology clinic between July 1, 2010 to January 1, 2012 on non-small cell lung cancer (NSCLC) pts at various times in their cancer trajectory and used the MST as the gold standard. Pts were asked the MST’s questions by an oncology dietitian and completed the pt - generated part of the PG-SGA. Cut off points of ≥ 2 for the MST and ≥ 4 for the PG-SGA ( pt-generated part) were used to identify pts at risk for malnutrition. Results: 144 pts with NSCLC (72 male, 72 female, aged 67 ± 11 years) completed the questionnaires. Sixty-nine percent (99/144) had advanced disease. Fifty-six percent (81/144) of pts had a score ≥ 2 on the MST and 70 % (101/144) pts had a score ≥ 4 on the PG-SGA ( pt generated part). In 114 cases, the MST and PG-SGA scores were congruent: 76 were true positive and 38 were true negative. And in 30 cases the scores were discordant: 25 were false positive and 5 were false negative. The PG-SGA had a sensitivity of 94 % and a specificity of 60% in comparison to the MST. Conclusions: The pt-generated part of the PG-SGA alone had a sensitivity of 94% when compared to the MST. This tool could be used to screen for malnutrition risk in NSCLC pts attending an out-patient oncology clinic.

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)0.016
metaresearch head score (Gemma)0.034
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 score0.016
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.034
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.253
GPT teacher head0.472
Teacher spread0.219 · 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
Published2012
Admission routes1
Has abstractyes

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