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Record W1968240659 · doi:10.1586/erp.12.41

Symptom clusters in patients with metastatic cancer: a literature review

2012· review· en· W1968240659 on OpenAlexafffund
Nemica Thavarajah, Emily Chen, Liang Zeng, Gillian Bedard, Julia Di Giovanni, Madeline Lemke, Natalie Lauzon, Michelle X. Zhou, Dominic Chu, Edward Chow

Bibliographic record

VenueExpert Review of Pharmacoeconomics & Outcomes Research · 2012
Typereview
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsHealth Sciences CentreSunnybrook Health Science Centre
FundersJoseph and Silvana Melara Cancer Research Fund
KeywordsCINAHLMedicineCluster (spacecraft)DemographicsMEDLINECancerPopulationMeta-analysisInternal medicinePsychiatryDemographyEnvironmental healthPsychological intervention

Abstract

fetched live from OpenAlex

This article reviews the literature reporting empirically determined symptom clusters in patients with metastatic cancer. A literature search was conducted on symptom clusters within heterogeneous metastatic cancer patient populations using MEDLINE, EMBASE, and CINAHL. Studies examining predetermined symptom clusters were excluded. A total of eight relevant studies published between 2005 and 2011 were identified. The number of symptom clusters extracted varied from two to eight clusters per study, comprising of two to eight symptoms per cluster. There were no clusters consistently identified within all eight studies. Notable differences in symptoms assessed, assessment tools, statistical analysis, patient demographics were observed between the studies. The lack of consensus among the inter-study symptom clusters are likely due to the differences in patient population as well as study methodology. Further exploration in metastatic symptom cluster research will ideally improve patient outcomes by facilitating improved symptom management in future clinical practice.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.561
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.058
GPT teacher head0.530
Teacher spread0.472 · 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.

Study designSystematic review
Domainnot available
GenreReview

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

Citations24
Published2012
Admission routes2
Has abstractyes

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