Considerations for chest clearance and cough augmentation in severe bulbar dysfunction: a case study
Bibliographic record
Abstract
This case study describes a 21-year-old male with congenital myotonic dystrophy referred to respiratory physiotherapy with a weak cough and upper respiratory tract secretions. Mechanical insufflation-exsufflation (MI-E) was prescribed. Post initiation, the patient described a worsening of secretions and increased attendances to hospital with suspected chest infection. He also described difficulties with speaking after use of MI-E. Multidisciplinary assessment of cough as well as bulbar and swallow function resulted in a primary diagnosis of oro-pharyngeal dysphagia as well as weak cough. An alternative prophylactic therapy programme including active cycle of breathing, chest wall percussions, and manually assisted cough, was prescribed to facilitate clearance of upper airway secretions and patient comfort. The case highlights some of the risks associated with cough augmentation techniques derived from single-discipline intervention in the neuromuscular patient population. Comprehensive multidisciplinary assessment and management were key to redefining this patient's diagnosis, allowing effective and individualised treatment.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| 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".