P4 Pneumothorax trends 2010–2020: a single centre retrospective study
Bibliographic record
Abstract
Introduction Work by Halifax et al in 2018 and 2022, suggested increasing inpatient burden of pneumothorax and widespread variation in management. Local trends have never been elucidated. Northumbria Healthcare NHS Foundation Trust (NHCT) has a well-established pleural service, serving just over 600,000. A retrospective cohort study was thus performed. Methods A coding search for ‘pneumothorax’ was performed for all patients attending NHCT between 2010 and 2020 was performed with local Caldicott approval. 1698 notes were analysed to exclude iatrogenic, traumatic and paediatric events. 580 remained and those were analysed- 183 primary pneumothoraces (PSP) and 397 secondary (SSP). Results Median age for PSP was 26.5 (IQR 35) with 69% male, and for SSP 68 years (IQR 68), 62% male. 23.5% of PSP and 8.6% of SSP were never smokers. Proportion of smokers and ex-smokers has not really changed over time: >65% every year have been smokers or ex-smokers. Yearly pneumothorax incidence shows a downward trend for PSP but upwards for SSP (figure 1). Median length of stay (LoS) for PSP was 2 (IQR 2), and SSP 5 (IQR 8), with a clear downward trend (figure 1). From 2010–2015 >50% PSP were managed with drain, but in 2019–2020 at least 50% managed conservatively, with significant reduction in aspiration. Trends of recurrence for PSP are increasing whereas for PSP is decreasing. 76 (20 PSP, 56 PSP) went for surgery at the index time with 5.3% recurrence (20% recurrence in those without surgery). Conclusions This is the first known analysis of pneumothorax trends in a large trust in the North East of England. The data has limitations (size of pneumothorax, frailty {opting for thus conservative management) not recorded), reliance on clinical coding and not all notes were available. Updated larger datasets should help elucidate trends better.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".