The Usefulness of CT Perfusion in Differentiation between Neoplastic and Tuberculous Disease of the Spine
Bibliographic record
Abstract
INTRODUCTION: Routine diagnostic techniques are not sufficient to confidently differentiate diseases of the axial skeleton. Purpose of study was to determine whether CT perfusion (CTP) can differentiate inflammatory diseases like tuberculosis from neoplastic diseases of spine. METHODS: Fifty-one patients with vertebrdraft%freshal body lesions associated with paraspinal mass underwent CT guided bone biopsy and histopathological evaluation. CTP was done before doing bone biopsy. Perfusion parameters like blood volume (BV), blood flow (BF), and time to peak (TTP) were calculated. Values are correlated with histopathological report of bone biopsy. Statistical analysis was done using Mann-Whitney test. P value < .05 was considered significant. RESULTS: Of 51, 32 had infective osteomyelitis and 19 neoplastic disease (9 metastasis, 5 plasmacytoma, 4 lymphoma and 1 chordoma. Mean rBF was [inflammatory lesions, 1.79 and neoplastic lesions, 9.42 (P < .000)]. Mean rBV was [inflammatory disease, 1.63 and neoplastic lesions, 9.37 (P < .000)]. CONCLUSION: CTP technique has potential for differentiating inflammatory from neoplastic lesions affecting spine associated with paraspinal mass noninvasively.
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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 | 0.005 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".