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Symptom recognition in advanced cancer. A comparison of nursing records against patient self‐rating

2001· article· en· W2125794331 on OpenAlexaboutno aff
Annette S Strömgren, M. Groenvold, Anita Rath Sørensen, L. Agerskov Andersen

Bibliographic record

VenueActa Anaesthesiologica Scandinavica · 2001
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
FundersKræftens Bekæmpelse
KeywordsMedicineNursing recordsCancerNursingFamily medicineMEDLINEInternal medicine

Abstract

fetched live from OpenAlex

The aim of the study was to investigate the extent to which the symptoms experienced by advanced cancer patients were covered by the nursing records. On the day of the first contact with our palliative care department, a nursing record was taken, and on this or the following day, 56 patients filled in the questionnaires EORTC Quality of Life Questionnaire (EORTC QLQ-C30), Edmonton Symptom Assessment System (ESAS), and Hospital Anxiety and Depression Scale (HADS). In each patient, the symptomatology reported in the patient-completed questionnaires was compared with the symptomatology mentioned by the nurse in the nursing record. The analysis revealed good concordance concerning pain and poor physical functioning, but patients reported other symptoms or problems much more often than their nurses. Reasons for these discrepancies are discussed. It is suggested that the nurse's knowledge of the patient's symptomatology might gain from more systematic screening or from transfer of information from patient self-assessment questionnaires to the nursing records.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.619
Threshold uncertainty score0.774

Codex and Gemma teacher scores by category

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

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 head0.045
GPT teacher head0.340
Teacher spread0.295 · 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

Citations103
Published2001
Admission routes1
Has abstractyes

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