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Record W1998149402 · doi:10.12669/pjms.293.3290

Symptoms and quality of life before and after stem cell transplantation in Cancer

2013· article· en· W1998149402 on OpenAlexaboutno aff
Özlem Ovayolu, Nimet Ovayolu, Emine Kaplan, Mustafa Pehlıvan, Gülendam Karadağ

Bibliographic record

VenuePakistan Journal of Medical Sciences · 2013
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineTransplantationQuality of life (healthcare)Stem cellDiseaseBone marrow transplantationInternal medicineGerontology

Abstract

fetched live from OpenAlex

OBJECTIVE: This study was conducted thinking that it was extremely important in terms of the disease and treatment to assess the symptoms that may be encountered before and after a stem cell transplantation and quality of life. METHODOLOGY: A prospective longitudinal design was used.The study was completed in two years on 82 patients who underwent transplantation at the bone marrow transplantation unit. Data were collected using a questionnaire, the Edmonton Symptom Assessment Scale, and the Short Form-36 quality of life scale. RESULTS: It was observed that the patients had low mean scores of physical and mental quality of life both before and after transplantation; there was an increase in the mean scores of all the symptoms and primarily of fatigue after the stem cell transplantation as compared to before it; and the mean scores of physical and mental quality of life further declined (p<0.05). CONCLUSION: Quality of life of patients who underwent stem cell transplantation is adversely affected in the periods immediately before and after transplantation. Patients' quality of life worsens as the severity of symptoms experienced by patients increases.

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)0.004
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.029
GPT teacher head0.344
Teacher spread0.316 · 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".

Quick stats

Citations27
Published2013
Admission routes1
Has abstractyes

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