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The prognostic value of degree of pre- and post-treatment anemia in patients with advanced gastroesophageal cancers.

2023· article· en· W4379335762 on OpenAlexaffabout
Malini Hu, Xin Wang, Yvonne Bach, Zeynep Baskurt, Hiroko Aoyama, Aruz Mesci, Carol J. Swallow, Marie‐Philippe Saltiel, Thais Baccili Cury Megid, Rebecca Wong, Jonathan Yeung, Patrick Veit‐Haibach, Sangeetha Kalimuthu, Eric Xueyu Chen, Savtaj S. Brar, Raymond Woo-Jun Jang, Elena Elimova

Bibliographic record

VenueJournal of Clinical Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicInflammatory Biomarkers in Disease Prognosis
Canadian institutionsToronto General HospitalMount Sinai HospitalPrincess Margaret Cancer CentreUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsMedicineInternal medicineAnemiaProportional hazards modelOncologyCancerChemotherapyNeutrophil to lymphocyte ratioProgression-free survivalOverall survival

Abstract

fetched live from OpenAlex

e16012 Background: Anemia is common in cancer patients and cancer-related anemia has been associated with poorer clinical outcomes in various malignancies. The degree of anemia in patients receiving chemotherapy, as a prognostic factor in gastroesophageal cancers (GE), is not well understood. There have been studies looking at clinical prognostic scores and their efficacy of predicting outcomes, of which the Gustave Roussy Immune (GRIm-Score) has been seen to be most predictive of early death in GE cancers. This study aims to compare hemoglobin (g/L) values before treatment and at multiple longitudinal timepoints during treatment as a prognostic marker of overall survival (OS) and progression-free survival (PFS). It also aims to identify whether various laboratory measures in the GRIm-Score, which includes lactate dehydrogenase (LDH), neutrophil to lymphocyte ratio (NLR) and body mass index (BMI) may hold prognostic value. Methods: A retrospective analysis of 171 patients with advanced GE cancer receiving first-line palliative-intent systemic therapy at the Princess Margaret Cancer Centre in Toronto, Canada from 2011 to 2021 was performed. Laboratory data were longitudinally collected across four time points: pre-chemotherapy, at first restaging scan, at disease progression and at final blood draw at last known follow up. Overall survival (OS) and progression-free survival (PFS) were estimated using the Kaplan-Meier method. Cox proportional hazards regression models were used to assess the association between change in hemoglobin from baseline, adjusted for LDH, NLR and BMI. Results: Mean hemoglobin value decreased with chemotherapy initiation at the first staging time point compared to pre-chemotherapy values by 10.82 g/L (-10.82, CI: -13.57, -8.07, p < 0.001). On univariate analysis, patients with a hemoglobin increase of 10 g/L at their final blood draw compared to pre-chemotherapy decreased their overall risk of death by 8.50% (HR 0.915, CI: 0.843-0.993, p = 0.033) as well as their PFS by 6.90% (HR 0.931, CI: 0.873-0.994, p = 0.031). On multivariable analysis, adjusting for relevant clinical factors including LDH, NLR and BMI, an increase in hemoglobin increase of 10 g/L from baseline to final was associated with better OS (HR 0.893, CI: 0.817-0.977, p = 0.01). On multivariate analysis, an increase in LDH at progression bloodwork compared to baseline levels was associated with poorer OS (HR 1.002, CI: 1.001-1.003, p = 0.026) and an increase in NLR at final blood draw compared to baseline was associated with poorer OS (HR 1.02, CI: 1.012-1.028, p < 0.001). Conclusions: Patients with metastatic GE cancers can develop treatment-associated anemia and this prognosticates a poorer overall survival. Prognostic models incorporating change in hemoglobin, LDH and NLR ratios should be considered in future clinical evaluation of metastatic GE cancers.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.021
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.036
GPT teacher head0.386
Teacher spread0.349 · 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
Published2023
Admission routes2
Has abstractyes

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