Exacerbation of asthma secondary to fentanyl transdermal patch: Table 1
Bibliographic record
Abstract
Asthma is a common chronic inflammatory disorder of the airways associated with hyperresponsiveness, reversible airflow limitation and respiratory symptoms.1 All patients with asthma are at risk for exacerbations that may range from mild to life threatening. Different triggers cause asthma exacerbation by inducing airway inflammation and/or provoking bronchospasm. Allergen-induced bronchospasm results from IgE-dependent release of mediators including histamine, prostaglandins and leukotrienes.2 Opiates are commonly used to treat chronic pain.3 Although hypersensitivity to opiates or accumulation of opiates can cause respiratory depression, opiates are also used in the management of cough and dyspnoea associated with advanced COPD and heart failure.4(,)5 Here, a report is presented on a patient who developed persistent exacerbation of underlying stable asthma after initiating fentanyl transdermal therapy for chronic low back pain. He underwent extensive investigations and a detailed reassessment of history, especially medication history, led to the possible causative factor; once recognised, removal of the offending agent (fenatnyl) resulted in complete improvement in his symptoms within 72 h.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".