Unmet need for biomarkers in chronic cough: a review of current challenges and future directions
Bibliographic record
Abstract
The diagnosis, investigation and management of chronic cough remains a challenge for patients and physicians. Patients with chronic cough can spend many years being investigated for possible underlying conditions and have numerous therapies before a diagnosis of refractory and unexplained chronic cough (RCC/UCC) is made. Recognising that RCC/UCC is a distinct disease with underlying neuro-pathological features which commonly present with clinical features of cough hypersensitivity syndrome would be key to improved management of these patients. Biomarkers are used across various specialities to aid diagnosis, direct treatment and monitor treatment response; however, at present, no such biomarkers exist for RCC/UCC. Biomarkers are needed for RCC/UCC that could potentially be used to identify and predict treatment response. Advances in the mechanisms and therapies for refractory chronic cough targeting the peripheral nerves expressing the P2X3 receptor, and opioid pathways in the central nervous system have provided hope for developing novel biomarkers. However, the development of biomarkers remains a challenging process and there is a need for ongoing research to address the lack of evidence in this area.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".