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Record W3112861454 · doi:10.31372/20200503.1106

Change in Body Weight and Serum Albumin Levels in Febrile Neutropenic Lung Cancer Patients

2020· article· en· W3112861454 on OpenAlexvenueno aff
Naomi Kayauchi, Yumi Nakagawa, T. Oteki, Katsunori Kagohashi, Hiroaki Satoh

Bibliographic record

VenueAsian/Pacific Island Nursing Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicNeutropenia and Cancer Infections
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineChemotherapyLung cancerInternal medicineNeutropeniaFebrile neutropeniaAlbuminSerum albuminGastroenterologyCancerRetrospective cohort studyComplicationSurgery

Abstract

fetched live from OpenAlex

Although advances have been made in the treatment and prevention of febrile neutropenia (FN) in cancer patients treated with chemotherapy, it is still a complication that requires clinical attention. Impaired nutritional status in patients who develop FN can affect the continuation of cancer treatment, but it has not been investigated. We conducted a retrospective longitudinal study in order to clarify (1) if body weight and serum albumin levels change in lung cancer patients who do and do not develop FN, and (2) if these indicators are more likely to worsen in patients with FN than in patients without FN. Patients undergoing cytotoxic chemotherapy between January 2011 and June 2020 were consecutively included in the study. Changes in body weight and serum albumin levels were investigated in a case-control study of patients with FN, and control patients without FN who were matched by age, gender, histopathology, and stage of lung cancer, at a ratio of 1:2. During the study period, 226 patients received cytotoxic chemotherapy. Among those, 33 (14.6%) patients developed FN during the first course of cytotoxic chemotherapy. We found a more pronounced decrease in both body weight and serum albumin level at four weeks after the initiation of chemotherapy in FN patients. In order to safely administer effective chemotherapy, medical staff need to pay close attention to the nutritional status of patients receiving chemotherapy.

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 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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.715

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.022
GPT teacher head0.303
Teacher spread0.281 · 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 teacher head, 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".

Quick stats

Citations4
Published2020
Admission routes1
Has abstractyes

Explore more

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