Communication after laryngectomy: An assessment of quality of life
Bibliographic record
Abstract
The purpose of this study was to examine quality of life in laryngectomees using different methods of communication. A survey was mailed to all the living laryngectomees in Nova Scotia. Patients were asked to rate their ability to communicate in a number of common situations, to rate their difficulty with several communication problems, and to complete the EORTC QLQ-C30 quality-of-life assessment tool. Sixty-two patients responded (return rate of 84%); 57% were using electrolaryngeal speech, 19% esophageal speech, and 8.5% tracheoesophageal speech. These groups were comparable with respect to age, sex, first language, education level, and years since laryngectomy. There were very few differences between these groups in ability to communicate in social situations and no difference in overall quality of life as measured by these scales. The most commonly cited problem was difficulty being heard in a noisy environment. Despite the fact that tracheoesophageal speech is objectively most intelligible, there does not seem to be a measurable improvement in quality of life or ability to communicate in everyday situations over electrolaryngeal or esophageal speakers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| 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 | 0.000 | 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".