A systematic review of patient‐reported outcome measures in head and neck cancer surgery
Bibliographic record
Abstract
OBJECTIVE: To identify, summarize, and evaluate patient-reported outcome questionnaires for use in head and neck cancer surgery with the view to making recommendations for future research. DATA SOURCES: A systematic review of the English-language literature, with the use of head-and-neck-surgery-specific keywords, was performed in the following databases: Medline, Embase, HAPI, CINAHL, Science/Social Sciences Citation Index, and PsycINFO from 1966 to March 2006. DATA EXTRACTION AND STUDY SELECTION: All English-language instruments identified as patient-reported outcome questionnaires that measure quality of life and/or satisfaction that had undergone development and validation in a head and neck cancer surgery population were included. DATA SYNTHESIS: Twelve patient-reported outcome questionnaires fulfilled our inclusion criteria. Of these, four were developed from expert opinion alone or did not have a published development process and seven questionnaires lacked formal item reduction. Only three questionnaires (EORTC Head and Neck Module, University of Michigan Head and Neck Quality-of-life Questionnaire, and Head and Neck Cancer Inventory) fulfilled guidelines for instrument development and evaluation as outlined by the Medical Outcomes Trust. CONCLUSIONS: Rigorous instrument development is important for creating valid, reliable, and responsive disease-specific questionnaires. As a direction for future instrument development, an increased focus on qualitative research to ensure patient input may help to better conceptualize and operationalize the variables most relevant to head and neck cancer surgery patients. In addition, the use of alternative methods of psychometric data analysis, such as Rasch, may improve the value of health measurement in clinical practice for individual patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.040 | 0.178 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.015 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".