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Record W4403987741 · doi:10.22543/2392-7674.1524

Assessment of psycho-emotional symptoms in cancer patients in an Oncology-Palliative Care Department from Romania

2024· article· en· W4403987741 on OpenAlexaboutno aff
Roxana Andreea Rahnea-Nita, Laura Rebegea, Elena Gabriela Vâlcu, Mihaela Dumitru, Radu-Valeriu Toma, Mihai Géorgescu, Georgia Luiza Serbanescu, Maria Barbu, Georgiana Bianca Constantin, G. Nita

Bibliographic record

VenueJournal of Mind and Medical Sciences · 2024
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePalliative careFamily medicinePsycho-oncologyCancerInternal medicineNursing

Abstract

fetched live from OpenAlex

Introduction. Anxiety and depression have an increased prevalence in cancer patients, especially in those in an advanced stage of the disease. These disorders have a major impact on the social life, existential concerns and quality of life of cancer patients. Materials and Methods. A number of 114 consecutive patients were included in the study (in a period of 2 weeks) who were screened for anxiety, depression and for other common symptoms, using Hospital Anxiety and Depression Scale (HADS) and Edmonton Symptom Assessment System (ESAS). Results. Regarding the age - the abnormal level of anxiety and depression: the percentage of patients over 65 years was higher than the percentage of patients under 64 years, both in terms of anxiety and depression. Regarding the Performance status ECOG - abnormal level of anxiety and depression: the percentage of patients with ECOG = 3-4 is higher than that of patients with ECOG = 0-2. Results. The increased prevalence of anxiety and depression requires psychological counseling and treatment. It is important for these symptoms to be identified as soon as possible, in order to provide a good quality of life. Conclusions. The model we propose is for the HADS to be a screening tool on admission to a palliative care ward, for certain categories of patients.

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 score0.111
Threshold uncertainty score0.580

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 integrity0.0000.000
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 head0.035
GPT teacher head0.414
Teacher spread0.380 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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