How Useful Are Bowel Sounds in Assessing the Abdomen?
Bibliographic record
Abstract
BACKGROUND: The purpose of our study is to determine the accuracy of bowel sounds in the diagnosis of ileus and bowel obstruction. METHODS: Healthy volunteers (n = 10) and patients with radiologically or laparotomy confirmed small bowel obstruction (n = 9) and ileus (n = 7) were enrolled. Two 30-second recordings from each subject were obtained using an electronic stethoscope. Study physicians (n = 20) were then presented with 43 recordings in blinded fashion and were asked whether each was from a normal subject or from a subject with bowel obstruction or ileus. RESULTS: Physicians arrived at the correct diagnosis a median of 30 times out of 43 (69.8%). Intra-observer variation (κ = 0.72, agreement 81.3%) and intra-subject variation (κ = 0.63, agreement 78.7%) were very good. Bowel sounds from subjects with ileus and normal bowel sounds were correctly identified most of the time (84.5 and 78.1%, respectively). Bowel sounds from patients with obstruction were correctly identified only 42.1% of the time, but if a physician believed he or she was hearing a bowel obstruction, this had a strong positive predictive value (PPV, 72.7%). CONCLUSION: Our results suggest that the auscultation of bowel sounds is useful, especially in detecting ileus. The diagnosis of obstruction had a high PPV.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".