Feasibility of locating tumours in lung via kinaesthetic feedback
Bibliographic record
Abstract
BACKGROUND: Localizing lung tumours during minimally invasive surgery is difficult, since restricted access precludes manual palpation and pre-operative imaging cannot map directly to the intra-operative lung. This study analyses the force-sensing performance that would allow an instrumented kinaesthetic probe to localize tumours based on stiffness variations of the lung parenchyma. METHODS: Agar injected into ex vivo porcine lungs produced a model approximating commonly encountered tumours. Force-deformation data were collected from multiple sites at various palpation depths and velocities, before and after the tumours were injected. RESULTS: Analysis showed an increase in force after the tumours were injected, in the range 0.07-0.16 N at 7 mm (p < 10(-4)). A 2 mm/s palpation velocity minimized exponential stress decay at constant depths, facilitating easier comparisons between measurements. CONCLUSION: A sensing range of 0-2 N, with 0.01 N resolution, should allow a kinaesthetic palpation probe to resolve local tissue stiffness changes that suggest an underlying tumour.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".