Bibliographic record
Abstract
Introduction：The use of Computer Tomography(CT)in pneumothorax patients remains controversy in practice guidelines.The incidence of visible blebs or bullae visible in chest was 70% in literature.We want to finger out that the recurrence of pneumothorax will related the present of blebs or bullae in CT in this study. Methods：We included 79 patients who received non contrast Computer Tomography before VATS for primary spontaneous pneumothorax from 2009 to 2013.They were between 15 and 30 years old and males dominant (93.6%) .We performed high resolution CT(HRCT)scans which provides 1.25 mm cross sections of the entire thorax.All the patients except for one male received thoracoscopic blebectomy(or bullectomy)and mechanical pleurodesis.We defined the recurrence of pneumothorax by medical record and telephone follow up.Results：The average following time was 732 days.There were 10(12.66%)ipsilateraland 9(11.39%)contralateralrecurrence of pneumothorax after primary surgery.The CT scan showed visible blebs or bullae in 89.74% in ipsilateral and 73.07% in contralateral thorax.For patients receiving surgery for primary spontaneous pneumothorax, the risk of contralateral recurrent pneumothorax is higher for those with contralateral bled vs those without with borderline statistical significance［5 year risk：18% vs 0%, log rank test p value =0.07］ .In subgroup analysis, the trend was similar for male［p=0.06］butnot female［no recurrence］ .The trend is also similar for non smoker［p=0.09］butless obvious for smoker［p=0.44］ . Conclusion：With the improvement of HRCT, we can read bullae and blebs more accurately in pneumothorax patients.We can use chest CT before regular blebectmy to predict the contralateral recurrence in the future.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".