Bibliographic record
Abstract
We would like to thank Uramoto et al . [ 1 ] for their interest and comments regarding our study [ 2 ]. The imaging characteristics of ultrasonography are determined by the velocity of propagation and the attenuation of ultrasound [ 3 ]. The ultrasonic velocity in air-containing lung is much slower than those in other tissues. Also, an air-containing lung absorbs and scatters ultrasonic energy, resulting in high attenuation in the lung. Our hypothesis is that less ultrasonic waves would reach the tumour in air-containing lungs due to these unfavourable properties, and the reflected ultrasonic waves would also be impaired, therefore, likely causing the indistinct border of the tumour. Conversely, when the lung is completely deflated, the aforementioned phenomena would be minimal, leading to successful transmission of ultrasonic waves. This makes it exceedingly important for the probe to be firmly pushed against the lung surface for successful visualization of the tumour. Our results show that all detectable pseudo-tumours were visualized within 14 mm from the probe surface in ultrasound images [ 2 ]. Another issue is that the ultrasonic velocity of completely deflated lung parenchyma, especially when the lung is strongly compressed, can become similar to that of the tumour. This may also affect the result of indistinct tumour borders of deeply located tumours. Real tumour visualization in the rabbit model demonstrated distinct hyperechoic border of the tumours; however, imaging characteristics of ultrasound of the tumour and the deflated lung were similar to each other. Hence, further advances in technology is necessary to ensure tumour visualization in the lung with high image quality. Elastography may be an option for lung tumour localization. This is a non-invasive imaging technology where the local tissue strains are calculated directly or indirectly in response to external mechanical stress [ 4 ]. It is clinically applied to plural organ cancer diagnosis, including breast, prostate, thyroid and pancreas; however, use for lung tumour localization is still under investigation. Uramoto et al . concluded that it has little diagnostic benefits for deeply located pseudo-tumours [ 1 ]. This was not encouraging; however, we still believe that there is room for improvement in their experimental settings. Firstly, were the lungs fully deflated? Again, air in the lung disturbs ultrasound images. It may affect elastography as well. The second concern is that their materials for pseudo-tumours may not be adequate for ultrasound evaluation as the probe needs to be pushed against the lung firmly. Real tumour evaluation would be more reliable. Lastly, target lesions for intraoperative localization should be located within 3–4 cm from the lung surface in the inflated lung. Our results showed that the depth in the deflated lung is less than half of that in the inflated lung. We believe that thoracoscopic ultrasound would be helpful for localization of tumours at a depth of 2 cm or less from the lung surface. We agree with the idea of elastography being applied to lung tumour localization and believe that further investigation is required to make any conclusions regarding the effectiveness of this technology in thoracic surgery. This work was supported by Olympus Medical Systems Corp. to Kazuhiro Yasufuku.
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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 | 0.013 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.024 | 0.020 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".