Heat and moisture exchanger use reduces in-hospital complications following total laryngectomy: A case–control study
Bibliographic record
Abstract
BACKGROUND: Total laryngectomy (TL) is an appropriate oncologic operation for many patients with laryngeal cancer delivering excellent oncologic outcomes, however it remains beset with significant functional consequences. Following TL, the upper and lower airways are permanently disconnected, which causes unfiltered, cold air with reduced humidity to enter the tracheobronchial tree, resulting in mucus overproduction and an increase in the viscosity of the mucus. In response to this, Heat and moisture exchangers were developed to compensate for the lost functions of the upper respiratory tract and their effect on the patients' respiratory performance in addition to their quality of life. METHODS: The case records of 48 patients undergoing total laryngectomy were reviewed and data concerning demographics, surgical details, post-operative care requirements and adverse events was retrieved. Post hoc analysis of the case patients was undertaken to identify any benefit of using a heat and moisture exchanger (HME) system with particular reference to post-operative respiratory outcomes. RESULTS: There was no significant difference between case and control subjects based on demographics, extent of surgery or need for flap repair. 16 patients had used a HME and 32 patients had used external humidification (EH). Of those experiencing mucous plugging, only 3/24 (12.5 %) had used a HME system, in contrast to 21/24 (87.5 %) who used EH (Chi square = 9.375, p = 0.002). The odds ratio of having an adverse event if not using HME was 8.27 (CI = 1.94 - 35.71). Use of HME also significantly reduced the number of days requiring physiotherapy (1.75 days vs. 3.20 days, p = 0.034). CONCLUSION: Use of an HME system can reduce in-hospital complications, in particular episodes of mucus plugging, and post-operative care requirements. Furthermore, there is a cost benefit to using HME systems that warrants more widespread introduction of these devices in head and neck surgery centers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".