Bibliographic record
Abstract
I would like to congratulate Drs. Thomas and MacDonald on their analysis of National Reporting and Learning System (NRLS) data relating to events in critical care 1. The article is very useful in pointing out both the strengths and weaknesses of the NRLS. Cross referencing against a known local dataset is of particular importance in identifying problems with robust analysis of the NRLS dataset. The authors did not find a reduction in airway incidents associated with harm in the three years following the national audit report in 2011 (35 incidents in 2008–2010, 34 incidents between 2012 and 2014). It is not clear whether the authors are implying that the Fourth National Audit Project (NAP4) 2, 3 has had no impact on such events, but readers might conclude this. It remains an important challenge for the NAP programme to identify whether these projects have led to demonstrable change in practice, and more importantly improvements in patient safety 8. A survey in 2011 identified that within a year of publication of NAP3, half of hospitals had changed information provided to patients and one quarter had changed clinical practices 9. Two years after the publication of NAP4, 98% of responding hospitals had made changes in clinical practice as a direct consequence of the publication 10. The Royal College of Anaesthetists and the Difficult Airway Society are currently discussing the use of better metrics for assessing the impact of NAP4 and future hospital-based data collection projects.
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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 | 0.110 | 0.462 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".