Bibliographic record
Abstract
Drug Delivery to the Lungs 21 was focused exclusively on delivery technologies of medicines for the treatment of diseases that are 'local' to the respiratory tract or for wider 'systemic' distribution. Therefore, the range of diseases that can be treated via delivering drugs to the lungs is large and diverse. This diversity means that the delivery technologies (device and/or formulation) are also very varied. Moreover, the patient is critically involved when using drug-delivery technologies to the lungs as their inhalation and 'user' characteristics are pivotal in ensuring that the correct dose is given and reaches the appropriate part of the respiratory tract. Thus, Drug Delivery to the Lungs 21 was a wide-ranging conference, ideal for an overview of current and future inhaled-delivery technologies. The conference was split into various themed sections and supported by approximately 65 posters. Furthermore, the conference was preceded by a workshop organized by the European Pharmaceutical Aerosol Group on abbreviated impactor measurement, which is a tool currently of much interest in assessing aerosol products (see separate summary). The conference initiated a number of innovations this year, including a Facebook page on which delegates and organizers could follow and 'chat' about conference proceedings.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.067 | 0.038 |
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".