The use of tracheostoma humidification by people with total laryngectomy in the UK: a cross-sectional survey.
Bibliographic record
Abstract
Objectives: To investigate the use of tracheostoma humidification by people with total laryngectomy (PTL) in the UK and explore influencing factors. Design: National cross-sectional survey and case note review. Setting: 26 UK National Health Service (NHS) centres providing care to PTL. Participants: PTL reviewed by speech and language therapy (SLT) between March and September 2020. Methods: Secondary analysis of data collected during a national multi-centre audit of PTL completed in response to the Covid-19 pandemic. Data were collected on type of humidification used by PTL and demographic information. Type of humidification was dichotomised as ‘HME’ (closed-system heat moisture exchanger) or ‘non-HME’ (alternative stoma cover or no stoma cover). Univariable analysis was performed to determine the association with several potential explanatory variables including gender, age, living circumstances, distance from treatment centre, communication method and time elapsed since laryngectomy. A backwards selection procedure was used to determine the final model for multiple regression analysis. Results: Data were obtained from 1216 PTL from 26 centres across the UK; information on type of tracheostoma humidification used was available for 1097 PTL. Most PTL (69%) used an HME. Following multiple regression analysis, time elapsed since laryngectomy (p=<0.001), living circumstances (p=0.002) and communication method (p=<0.001) were statistically significant factors in HME use. Conclusion: In the UK, most PTL follow recommendations to use a closed-system HME, though there is marked variability across centres. HME use is influenced by time elapsed since laryngectomy, living circumstances and communication method.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".