Additional value of combining low-dose computed tomography to V/Q SPECT on a hybrid SPECT-CT camera for pulmonary embolism diagnosis
Bibliographic record
Abstract
OBJECTIVES: The aim of the study was to assess the potential interest of combining a low-dose computed tomography (ldCT) to ventilation/perfusion (V/Q) single-photon emission computed tomography (SPECT) for the diagnosis of pulmonary embolism (PE). We addressed three main questions: Could ldCT be used in substitution to ventilation SPECT? Could ldCT improve the diagnostic performance of V/Q SPECT? Could ldCT provide alternative diagnoses to PE? METHODS: A total of 393 patients previously analysed in a management outcome study that aimed at assessing the safety of V/Q SPECT for PE diagnosis were assessed. All patients underwent an ldCT under the same SPECT-computed tomography camera, which was not used at the time of initial interpretation. Three retrospective analyses were performed: Q SPECT combined with ldCT, V/Q SPECT combined with ldCT and ldCT only. RESULTS: On the basis of initial V/Q SPECT interpretation, 110 (28%) patients were positive and 283 (72%) were negative for PE.With Q SPECT-ldCT, 139 (35%) patients were positive and 254 (65%) were negative, with 55 (19%) discrepancies when compared with V/Q SPECT. Of the 283 patients with negative V/Q SPECT, 42 were positive with V/Q SPECT-ldCT, and among the 110 patients with positive V/Q SPECT 13 were negative with V/Q SPECT-ldCT. On using V/Q SPECT-ldCT, 97 (25%) patients were positive and 296 (75%) were negative, with 13 (3%) discrepancies when compared with V/Q SPECT (all had had a positive V/Q SPECT but a negative V/Q SPECT-ldCT). Finally, 67 (24%) ldCT scans showed a potential alternative diagnosis to PE. CONCLUSION: For PE diagnosis with lung SPECT, the use of ldCT in substitution to ventilation SPECT is associated with a high risk of overdiagnosis. The diagnostic value of ldCT in addition to V/Q SPECT remains unclear. Further studies are needed to determine its potential role in PE diagnosis.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".