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Record W2804283497 · doi:10.1002/hsr2.48

Development and evaluation of screening dysphagia tools for observational studies and routine care in cancer patients

2018· article· en· W2804283497 on OpenAlexafffund
Kishan Shani, M. Catherine Brown, Judy Chen, Mindy Liang, Joel Karkada, Simranjit Kooner, Mark Doherty, Grainne M. O’Kane, Raymond Jang, Elena Elimova, Rebecca Wong, Gail Darling, Wei Xu, Doris Howell, Geoffrey Liu

Bibliographic record

VenueHealth Science Reports · 2018
Typearticle
Languageen
FieldHealth Professions
TopicDysphagia Assessment and Management
Canadian institutionsUniversity Health NetworkOccupational Cancer Research CentrePrincess Margaret Cancer CentrePublic Health OntarioUniversity of Toronto
FundersPrincess Margaret Cancer FoundationCancer Care Ontario
KeywordsDysphagiaMedicineSwallowingObservational studyPhysical therapyOdynophagiaGold standard (test)Common Terminology Criteria for Adverse EventsAdverse effectCancerHead and neck cancerIntensive care medicineSurgeryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND AND AIMS: Dysphagia can be associated with significant morbidity in cancer patients. We aimed to develop and evaluate dysphagia screener tools for use in observational studies (phase 1) and for routine symptom monitoring in clinical care (phase 2). METHODS: Various dysphagia or odynophagia screening questions, selected after an expert panel reviewed the content, criterion, and construct validity, were compared with either functional assessment of cancer therapy - esophageal cancer (FACT-E) Swallowing Index Cut-Off Values or to questions adapted from the Patient Reported Outcomes for Common Terminology Criteria for Adverse Events. Sensitivity, specificity, and patient acceptability were assessed. RESULTS: In Phase 1 (n = 178 esophageal cancer patients), the screening question "How are you currently eating?" had the highest sensitivities and specificities against various Swallowing Index Cut-Off Value cut-offs, with the best optimal cutoff associated with weight loss (80% sensitivity and 75% specificity). In phase 2 (255 head and neck, gastro-esophageal, and thoracic cancer patients), a single question screener ("Do you experience any difficulty or pain upon swallowing?") versus a Patient Reported Outcomes for Common Terminology Criteria for Adverse Events-like gold standard generated sensitivities between 86% and 94% and specificities between 93% and 100%. This screening question (+/- follow-up questions) had a median completion time of under 2 minutes, and >90% of patients were willing to complete the survey electronically, did not feel that survey made clinic visit more difficult, and did not find the questions upsetting or distressful. CONCLUSION: Our results demonstrate that these screener tools ("How are you currently eating?", "Do you experience any difficulty or pain upon swallowing?") can effectively screen dysphagia symptoms without increasing cancer outpatient clinic burden, both in observational studies and for routine clinical monitoring.

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)0.217
metaresearch head score (Gemma)0.303
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.217
Threshold uncertainty score0.965

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2170.303
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0050.004
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.003
Research integrity0.0010.001
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 head0.409
GPT teacher head0.567
Teacher spread0.158 · 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.

Study designObservational
Domainnot available
GenreMethods

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

Citations4
Published2018
Admission routes2
Has abstractyes

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