Patient-specific targeted bronchial thermoplasty: predictions of improved outcomes with structure-guided treatment
Bibliographic record
Abstract
Bronchial thermoplasty is a recent treatment for asthma in which ablative thermal energy is delivered to specific large airways according to clinical guidelines. Therefore, current practice is effectively "blind," as it is not informed by patient-specific data. The present study seeks to establish whether a patient-specific approach based on structural or functional patient data can improve outcomes and/or reduce the number of procedures required for clinical efficacy. We employed a combination of extensive human lung specimens and novel computational methods to predict bronchial thermoplasty outcomes guided by structural or functional data compared with current clinical practice. Response to bronchial thermoplasty was determined from changes in airway responses to strong bronchoconstrictor simulations and flow heterogeneity after one or three simulated thermoplasty procedures. Structure-guided treatment showed significant improvement over current unguided clinical practice, with a single session of structure-guided treatment producing improvements comparable with three sessions of unguided treatment. In comparison, function-guided treatment did not produce a significant improvement over current practice. Structure-guided targeting of bronchial thermoplasty is a promising avenue for improving therapy and reinforces the need for advanced imaging technologies. The functional imaging-guided approach is predicted to be less effective presently, and we make recommendations on how this approach could be improved. NEW & NOTEWORTHY Bronchial thermoplasty is a recent treatment for asthma in which thermal energy is delivered via bronchoscope to specific airways in an effort to directly target airway smooth muscle. Current practice involves the treatment of a standard set of airways, unguided by patient-specific data. We consider the potential for guided treatments, either by functional or structural data from the lung, and show that treatment guided by structural data has the potential to improve clinical practice.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".