Comparing Puncture-Detection Approaches for Manual Needle Insertions Through the Parietal Pleura
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Bibliographic record
Abstract
Tube thoracostomy (chest tube insertion) is a surgical procedure that treats pneumothorax, a potentially life-threatening condition where air accumulates between the chest wall and the lungs. The literature reports high complication rates for this procedure, including accidental fatality due to poor manual depth control during tool insertion. We hypothesize that an instrumented needle-holder could help operators recognize pleural puncture and improve depth control, and we present a puncture-detection experiment that contributes toward this goal. An operator manually inserted a bevel-tip needle into ex vivo porcine ribs and through the parietal pleura via a sensorized percutaneous device that records position, force, and videos. We use this rich dataset of 63 insertions to thoroughly test four previously published data-driven puncture-detection (DDPD) algorithms against two new real-time algorithms: a custom recursive digital filter with coefficients optimized for our application, and a difference equation that compares standard deviations between adjacent sliding windows. Our algorithms achieve a precision (true positives over total identified punctures) of 23% and 22%, respectively, while the precision of existing DDPD algorithms ranges from 0% to 21%. Despite these performance improvements, our results show the limitations of DDPD algorithms and motivate new methods for detecting pleural membrane punctures in thoracostomy.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it