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Record W3161423651 · doi:10.14740/wjon1371

Quality of Life and Psychological Distress of Lung Cancer Patients Undergoing Chemotherapy

2021· article· en· W3161423651 on OpenAlexvenueno aff
Paraskevi Maria Prapa, Ιωάννα Παπαθανασίου, Vissarion Bakalis, Foteini Malli, Dimitrios Papagiannis, Εvangelos C. Fradelos

Bibliographic record

VenueWorld Journal of Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAnxietyQuality of life (healthcare)Lung cancerDistressPsychological distressDepression (economics)DiseaseInternal medicineAdverse effectPhysical therapyClinical psychologyPsychiatryNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Patients with lung cancer often experience multiple symptoms associated with both the disease itself and the treatment. The disease and therapy-related adverse effects may lead to poor quality of life (QoL) and increased psychological distress. The aim of this study was to investigate the QoL and psychological distress of patients with lung cancer. The relationship between these two aspects was also an area of focus. METHODS: This was a quantitative descriptive study. Data collection was done using a self-complementary tool. The data were collected between February and March 2020. The sample consisted of 135 patients with lung cancer who were undergoing chemotherapy in 1-day clinic in Athens (a sample of convenience). RESULTS: Regarding the QoL of our sample, we observed that the mean score of the physical health component of SF-12 was 38.17 ± 9.94 and of the mental health component was 45.63 ± 11.80. As regards the psychological distress of our sample, we observed that the mean score for depression was 4.55 ± 5.04, for anxiety was 3.84 ± 4.17 and for stress was 5.21 ± 5.01. CONCLUSION: As is clear from the results, lung cancer patients reported poor QoL and increased rates of psychological distress.

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 score0.042
Threshold uncertainty score0.379

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 head0.046
GPT teacher head0.402
Teacher spread0.357 · 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

Citations54
Published2021
Admission routes1
Has abstractyes

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