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Record W3200136458 · doi:10.3390/jcm10184162

Identifying Patient-Reported Outcome Measures (PROMs) for Routine Surveillance of Physical and Emotional Symptoms in Head and Neck Cancer Populations: A Systematic Review

2021· review· en· W3200136458 on OpenAlexaff
Sheilla de Oliveira Faria, Gillian Hurwitz, Jaemin Kim, Jacqueline Liberty, Kimberly Orchard, Geoffrey Liu, Lisa Barbera, Doris Howell

Bibliographic record

VenueJournal of Clinical Medicine · 2021
Typereview
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsUniversity of CalgaryCancer Care OntarioPrincess Margaret Cancer CentrePublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsMedicineCINAHLPatient-reported outcomeHead and neck cancerMEDLINEReliability (semiconductor)Systematic reviewSample size determinationClinical psychologyPhysical therapyCancerPsychiatryQuality of life (healthcare)Internal medicinePsychological intervention

Abstract

fetched live from OpenAlex

The aims of this review were to identify symptoms experienced by head and neck cancer (HNC) patients and their prevalence, as well as to compare symptom coverage identified in HNC specific patient-reported outcome measures (PROMs). Searches of Ovid Medline, Embase, PsychInfo, and CINAHL were conducted to identify studies. The search revealed 4569 unique articles and identified 115 eligible studies. The prevalence of reported symptoms was highly variable among included studies. Variability in sample size, timing of the assessments, and the use of different measures was noted across studies. Content mapping of commonly used PROMs showed variability and poor capture of prevalent symptoms, even though validation studies confirmed satisfactory reliability and validity. This suggests limitations of some of the tools in providing an accurate and comprehensive picture of the patient's symptoms and problems.

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)0.004
metaresearch head score (Gemma)0.020
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.142
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0120.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.351
GPT teacher head0.543
Teacher spread0.192 · 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

Citations22
Published2021
Admission routes1
Has abstractyes

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