Noninvasive Verification of Nasogastric Tube Placement Using a Magnet‐Tracking System: A Pilot Study in Healthy Subjects
Bibliographic record
Abstract
BACKGROUND: Fluoroscopic verification of nasogastric (NG) feeding tube placement is inconvenient and involves radiation exposure. We tested whether the position of an NG tube can be assessed reliably by a recently introduced magnet-tracking system. METHODS: A small permanent magnet was attached at the end of an NG tube and its position was monitored using an external sensor array connected to a computer. NG tube trajectory, spontaneous movements of the magnet, and its position relative to the lower esophageal sphincter (LES) and xiphisternum were assessed in 22 healthy subjects and compared with esophageal manometry. In 12 subjects, localization of the magnet was also compared with fluoroscopy. RESULTS: Magnet-tracking displayed NG tube tip movement reproducibly as it moved vertically in the esophagus and then laterally into the stomach. Compared with manometry, the accuracy and sensitivity of magnet tracking for localization of the NG tube tip, above or below the diaphragm, were 100%. Compared with fluoroscopy, the accuracy of NG tube localization by magnet tracking was 100%. With the magnet in the stomach, but not in the esophagus or LES, low amplitude displacements at a frequency of 3 per minute, consistent with gastric slow wave activity, were observed. CONCLUSIONS: Magnet tracking allows accurate, real-time, 3-dimensional localization of an NG tube with respect to anatomic landmarks. Recorded motor patterns are indicative of the position of the NG tube. Magnet tracking may be a useful tool for bedside placement of nasogastric and enteral feeding tubes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".