Multi‐dimensional phenotyping: towards a new taxonomy for airway disease
Bibliographic record
Abstract
All the real knowledge which we possess, depends on methods by which we distinguish the similar from the dissimilar. The greater the number of natural distinctions this method comprehends the clearer becomes our idea of things. The more numerous the objects which employ our attention the more difficult it becomes to form such a method and the more necessary. Classification is a fundamental part of medicine. Diseases are often categorized according to pre-20th century descriptions and concepts of disease based on symptoms, signs and functional abnormalities rather than on underlying pathogenesis. Where the aetiology of disease has been revealed (for example in the infectious diseases) a more precise classification has become possible, but in the chronic inflammatory diseases, and in the inflammatory airway diseases in particular, where pathogenesis has been stubbornly difficult to elucidate, we still use broad descriptive terms such as asthma and chronic obstructive pulmonary disease, which defy precise definition because they encompass a wide spectrum of presentations and physiological and cellular abnormalities. It is our contention that these broad-brush terms have outlived their usefulness and that we should be looking to create a new taxonomy of airway disease-a taxonomy that more closely reflects the spectrum of phenotypes that are encompassed within the term airway inflammatory diseases, and that gives full recognition to late 20th and 21st century insights into the disordered physiology and cell biology that characterizes these conditions in the expectation that these will map more closely to both aetiology and response to treatment. Development of this taxonomy will require a much more complete and sophisticated correlation of the many variables that make up a condition than has been usual to employ in an approach that encompasses multi-dimensional phenotyping and uses complex statistical tools such as cluster analysis.
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 | 0.018 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.016 | 0.009 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.012 | 0.024 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.005 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".