Outcomes Following Surgical Lung Biopsy for Interstitial Lung Diseases: A Monocenter Experience
Bibliographic record
Abstract
BACKGROUND: Surgical lung biopsy (SLB) is considered in the investigation of interstitial lung diseases (ILDs) when a complete clinical evaluation and a multidisciplinary discussion (MDD) do not allow the clinician to make a confident diagnosis. Owing to the risk of the procedure, an appropriate assessment of the risk/benefit ratio prior to the intervention is recommended. We aimed to assess the postoperative outcomes and diagnostic yield of SLB for the investigation of ILD in a tertiary care institution. METHODS: We conducted a retrospective cohort study of consecutive subjects who underwent a SLB for the investigation of ILD in our center from 2009 to 2020. The postoperative mortality and complications rates as well as the diagnostic yield of the procedure were assessed. RESULTS: Of the 1,805 patients newly investigated for ILD in our center from 2009 to 2020, 71 (3.93%) underwent a SLB. At days 30 and 90, the mortality rates were 0 and 2.8%, whereas 4.3 and 7.6% patients experienced an acute ILD exacerbation, respectively. In addition, 4 (5.8%) patients experienced infectious complications and 5 (7.0%) presented prolonged air leaks (all within 30 days). A definite pathological diagnosis was made in 47 (66.2%) patients. Following postoperative MDD, a confident diagnosis was made in 61 patients (85.9%) and resulted in a change of therapy in 49 (69.0%) patients. CONCLUSION: SLB for the diagnosis of unclassifiable ILDs is associated with low mortality but significant morbidity. However, it results in a confident diagnosis and a change in therapy in the majority of patients.
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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 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".