Diagnostic Value of <sup>99m</sup> Tc-Ubiquicidin Scintigraphy in Differentiating Bacterial from Nonbacterial Pneumonia
Bibliographic record
Abstract
Purpose:Differentiating purely viral from bacterial etiologies continues to be a challenging yet key step in the management of community-acquired pneumonia (CAP), further highlighted since the COVID-19 pandemic. This study aims to evaluate the utility of 99mTc-ubiquicidin (UBI) in the differentiation of bacterial from nonbacterial pneumonia. Methods:A total of 30 patients with CAP were allocated into groups A, bacterial (n = 15), and B, viral pneumonia (n = 15). All patients underwent 99mTc-UBI scan with planar and single-photon emission computed tomography (SPECT) images of thorax acquired at 30 and 180 min postinjection. Target-to-background (T/B) ratios were calculated with values >1.4 interpreted as positive for bacterial infection. Correlation was made with computed tomography (CT) scan and polymerase chain reaction (PCR) results. Results:UBI scan was positive in 43.3% (n = 13) of patients, with sensitivity, specificity, and accuracy of 86.7%, 100%, and 93.3%, respectively, and close correlation with chest CT scan and PCR results (p-value = 0.000). Planar images were generally not helpful. Receiver operating characteristic curve analysis indicated similar diagnostic performance for 30-min and 3-h SPECT images by implementing T/B thresholds of 1.2 and 1.33, respectively. Conclusions:99mTc-UBI SPECT is a promising modality for differentiating purely viral from bacterial or superimposed bacterial pneumonia and provides reliable evidence either to mandate or withhold administration of antibiotics in patients with CAP.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".