Transthoracic needle biopsy versus surgical diagnosis for solid pulmonary nodules
Bibliographic record
Abstract
Background: When investigating solitary pulmonary nodules (SPN), non-surgical [such as transthoracic needle biopsy (TTNB)] or surgical biopsies can be performed. There is a paucity of data comparing these two approaches. Methods: This descriptive study is a retrospective analysis of a cohort of 149 patients who underwent TTNB and/or surgery for a SPN >8 mm but ≤3 cm between January and December 2016, at Institut universitaire de cardiologie et de pneumologie de Québec (IUCPQ). Our primary objective was to evaluate the proportion of patients investigated with TTNB vs. surgical diagnosis in our center. Secondary objectives were to examine the distribution of diagnoses (malignancy vs. benign lesion), to evaluate the proportion of TTNB that would yield a benign diagnosis and permit to avoid surgery, to evaluate if delays to surgery were longer when preoperative TTNB was performed, and if operative times were longer with upfront surgery. Results: In our cohort, 87 patients (58%) underwent TTNB, while 62 (42%) had an upfront surgical procedure. One hundred and twenty-eight patients (86%) had a malignant diagnosis. Thirteen patients out of the 87 biopsied (15%) avoided surgery owing to a benign biopsy result, or a non-specific diagnosis and a physician reassured enough to decide for radiological surveillance, while 5/62 patients (8%) who underwent upfront surgery had a benign diagnosis. There was no significant difference in delays from imaging to surgery or in operative time between patients with or without prior TTNB. Conclusions: In this unicentric retrospective cohort of patients investigated for SPN, the malignancy rate was high (86%), which seemed to limit the applicability of prediction models. Adherence to guidelines for the investigation of SPN by physicians seemed suboptimal. More real-world prospective studies are needed to compare non-surgical and surgical biopsies. There is also a need for simpler nodule evaluation algorithms.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".