Proton magnetic resonance spectroscopy as a potential tool for differentiating between abdominal fluid collections
Bibliographic record
Abstract
PURPOSE: To determine the utility of proton magnetic resonance spectroscopy (MRS) in distinguishing abdominal fluid types. MATERIALS AND METHODS: Abdominal fluid samples were obtained from patients undergoing therapeutic percutaneous drainage. In vitro spectroscopy was performed using a 1.5-T scanner and a head coil. Single voxel spectra were obtained using a point resolved spin-echo sequence with water suppression (TR/TE 2000 msec/35 msec). The peak pattern for each sample was examined and the signal-to-noise ratio (SNR) estimated (ratio of tallest peak to noise at <0 ppm). RESULTS: Thirty-five samples were analyzed: purulent collection (eight), serosanguinous collection (eight), non-chylous ascites (six), chylous ascites (one), bile (seven), and bile with iodinated contrast media (five). The mean SNR of the dominant peak was: purulent collection, 12.7; serosanguinous collection, 3.2; non-chylous ascites, 2.4; chylous ascites, 8.8; bile, 1.4; and bile with contrast media, 60.8. Pus samples had a broad based peak pattern with continuous signal of >1.5 ppm width situated within the range 0.2-2.5 ppm, not found in other samples. Chylous ascites (one sample) had a distinctive peak at 1.2 ppm. Bile with contrast had three peaks at 3.5/3.6, 2.6, and 2.1 ppm. No other patterns were found to be discriminatory. Common non-specific patterns seen included a bifid peak at 1.1-1.3 ppm and a broad based peak situated between 3 and 4 ppm. CONCLUSIONS: The H1 spectra of purulent fluid has a higher SNR than common non-purulent abdominal fluids and a distinct broad based peak pattern from 0.2-2.5 ppm. Proton spectroscopy may be a useful tool for distinguishing purulent from non-purulent intra-abdominal collections.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".