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A structured review of quality of life instruments for head and neck cancer patients

2001· review· en· W1969558505 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHead & Neck · 2001
Typereview
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsUniversity of TorontoPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsMedicineQuality of life (healthcare)Head and neck cancerReliability (semiconductor)DiseaseHead and neckMEDLINEPhysical therapyMedical physicsCancerIntensive care medicineSurgeryPathologyInternal medicineNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Quality of life (QOL) is an important treatment outcome for head and neck cancer. Our purpose was to critically review published disease-specific QOL instruments. METHODS: Medline and Cancerlit were searched from 1966-1999. Eight disease-specific QOL instruments were identified, described, and appraised for development, sensibility, reliability, validity and responsiveness to change. RESULTS: Several of the available instruments have been well-developed and characterized. No one instrument is ideal for all purposes. When selecting a disease-specific QOL instrument for head and neck cancer patients, careful consideration must be given to disease subsite, treatment, timing of assessment, clinical setting, study purpose and research question. CONCLUSION: Validation of QOL instruments is an ongoing process. Direct comparisons of different instruments may help to establish the most appropriate questionnaire for each situation. Efforts should be focused on the evaluation of existing instruments, rather than the development of new questionnaires.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.711
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0000.000
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 head0.120
GPT teacher head0.438
Teacher spread0.318 · 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