Bibliographic record
Abstract
It is not widely known that valve disease is as common as heart failure, with a prevalence of 2.5% in the general population and over 10% in those aged >75.1 There are many well-established national programmes for heart failure yet none exist for valve disease, which can justifiably be regarded as the ‘next cardiac epidemic waiting to happen’.2 There are many limitations in our care for heart valve disease. Most patients are still cared for by general physicians or GPs without specialist expertise despite management decisions becoming increasingly complex.3 One-half of patients throughout Europe receive surgery too late.4 The situation is particularly poor for older people, at least 30% of whom are not referred even when clinically indicated.3 There is unacceptable variation in access to aortic valve surgery in the UK,5 particularly in London where Camden has observed activity 47% above age-predicted rates and Brent has activity 40% below age-predicted rates. These limitations have led to a call for specialist valve clinics.3,6 These are expected to improve the assessment of valve disease and the timing of surgery, and to ensure referral to an appropriately qualified and …
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 | 0.016 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.019 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.051 | 0.020 |
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".