Efficacy of Thickened Liquids for Eliminating Aspiration in Head and Neck Cancer
Bibliographic record
Abstract
OBJECTIVE: To appraise the current videofluoroscopic evidence on the reduction of aspiration using thickened liquids in the head and neck cancer population. DATA SOURCES: Search terms relating to deglutition or dysphagia or swallow and neoplasms and oncology or head and neck cancer and viscosity or texture and apira or residu* were combined with honey or nectar, xerostomia, and respiratory aspiration using Boolean operators. REVIEW METHODS: A multiengine literature search identified 337 nonduplicate articles, of which 6 were judged to be relevant. These underwent detailed review for study quality and qualitative synthesis. RESULTS: The articles reviewed in detail predominantly described heterogeneous study samples with small sample sizes, making for difficult interpretation and generalization of results. Rates of aspiration were typically not reported by bolus consistency, despite the fact that a variety of stimulus consistencies was used during a videofluoroscopic swallowing study. Studies confirmed that aspiration is a major concern in the head and neck cancer population and reported a trend toward more frequent aspiration after (chemo)radiotherapy. CONCLUSION: Overall, the literature on thickened liquids as an intervention to eliminate aspiration in the head and neck cancer population is limited. Because aspiration is known to be prevalent in the head and neck cancer population and thickened liquids are known to eliminate aspiration in other populations, it is important to determine the effectiveness of thickened liquids for reducing aspiration in the head and neck cancer population.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".