Investigating Swallowing and Tracheostomy Following Critical Illness: A Scoping Review
Bibliographic record
Abstract
OBJECTIVES: Tracheostomy and dysphagia often coexist during critical illness; however, given the patient's medical complexity, understanding the evidence to optimize swallowing assessment and intervention is challenging. The objective of this scoping review is to describe and explore the literature surrounding swallowing and tracheostomy in the acute care setting. DATA SOURCES: Eight electronic databases were searched from inception to May 2017 inclusive, using a search strategy designed by an information scientist. We conducted manual searching of 10 journals, nine gray literature repositories, and forward and backward citation chasing. STUDY SELECTION: Two blinded reviewers determined eligibility according to inclusion criteria: English-language studies reporting on swallowing or dysphagia in adults (≥ 17 yr old) who had undergone tracheostomy placement while in acute care. Patients with head and/or neck cancer diagnoses were excluded. DATA EXTRACTION: We extracted data using a form designed a priori and conducted descriptive analyses. DATA SYNTHESIS: We identified 6,396 citations, of which 725 articles were reviewed and 85 (N) met inclusion criteria. We stratified studies according to content domains with some featuring in multiple categories: dysphagia frequency (n = 38), swallowing physiology (n = 27), risk factors (n = 31), interventions (n = 21), and assessment comparisons (n = 12) and by patient etiology. Sample sizes (with tracheostomy) ranged from 10 to 3,320, and dysphagia frequency ranged from 11% to 93% in studies with consecutive sampling. Study design, sampling method, assessment methods, and interpretation approach varied significantly across studies. CONCLUSIONS: The evidence base surrounding this subject is diverse, complicated by heterogeneous patient selection methods, design, and reporting. We suggest ways the evidence base may be developed.
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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 | 0.019 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.021 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".