ECFS best practice guidelines: the 2018 revision
Bibliographic record
Abstract
Developments in managing CF continue to drive dramatic improvements in survival. As newborn screening rolls-out across Europe, CF centres are increasingly caring for cohorts of patients who have minimal lung disease on diagnosis. With the introduction of mutation-specific therapies and the prospect of truly personalised medicine, patients have the potential to enjoy good quality of life in adulthood with ever-increasing life expectancy. The landmark Standards of Care published in 2005 set out what high quality CF care is and how it can be delivered throughout Europe. This underwent a fundamental re-write in 2014, resulting in three documents; center framework, quality management and best practice guidelines. This document is a revision of the latter, updating standards for best practice in key aspects of CF care, in the context of a fast-moving and dynamic field. In continuing to give a broad overview of the standards expected for newborn screening, diagnosis, preventative treatment of lung disease, nutrition, complications, transplant/end of life care and psychological support, this consensus on best practice is expected to prove useful to clinical teams both in countries where CF care is developing and those with established CF centres. The document is an ECFS product and endorsed by the CF Network in ERN LUNG and CF Europe.
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 | 0.015 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.016 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.032 | 0.018 |
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".