Methods of functional outcomes assessment following treatment of oral and oropharyngeal cancer: review of the literature.
Bibliographic record
Abstract
OBJECTIVE: The purpose of this article is to review and document the differing methods of functional outcome measurements following treatment for oral and oropharyngeal carcinoma. STUDY DESIGN: Literature review. METHODS: Articles were identified using the MEDLINE database search engine. The "Methods" sections of relevant articles were then reviewed, and functional outcomes assessment methods were tabulated. RESULTS: We identified 60 articles published in the last 7 years (2000-2007) that reported on functional outcomes following treatment for oral or oropharyngeal cancer. Twenty-three studies used quality of life questionnaires and 12 used clinical observations to describe function. Swallowing was assessed objectively in 29 studies, with videofluoroscopic swallowing studies as the primary method of assessment. Speech was assessed in only 10 articles, with perceptual analysis used as the primary assessment modality. CONCLUSIONS: Preserving good speech and swallowing function following treatment for oral and oropharyngeal cancer remains an extremely important aspect of cancer care. Nevertheless, there is a clear lack of uniform methods for assessing functional outcomes. We propose that functional outcome studies should include both objective and subjective assessments of swallowing and speech to gain sufficient information on posttreatment function.
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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 | 0.016 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".