QI project: prospective evaluation of the impact of palliative care delivery by interstitial lung disease clinical nurse specialists within a tertiary specialist centre.
Bibliographic record
Abstract
Introduction: Interstitial lung disease (ILD) is a group of progressive conditions with a poor prognosis between 3-5 years. Delayed access to palliative care may contribute to negative health outcomes and create barriers in accessing wider services. Aims & objectives: This QI project aimed to increase the percentage of patients attending a first appointment in the ILD palliative service within the local target aim from 0% at baseline data collection to 80% by project closure through ILD CNS-led care delivery. Methods: PDSA cycles in order to achieve the project aim included an in-clinic screening tool, dedicated ILD nurse-led clinics and ongoing education to promote knowledge of service provision. Changes in patient waiting-times were observed, with patient satisfaction and clinic utilisation monitored as outcome measures. Conclusions: 39% of patients attended a first appointment within target timeframe. Nil differences between nurse-led and consultant-led patient satisfaction were identified. Whilst the project aim was not achieved, the total percentage of patients seen within the target timeframe improved from 0% to 48.4%. These results suggest ILD CNS-led clinics have the potential to significantly reduce waiting times to access palliative care.
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".