Evaluation of the peritoneal carcinomatosis index with CT and MRI
Bibliographic record
Abstract
Abstract Background The aim was to determine the incremental value of MRI compared with CT in the preoperative estimation of the peritoneal carcinomatosis index (PCI). Methods CT and MRI examinations of patients with peritoneal carcinomatosis were evaluated. CT images were first analysed by two observers who determined a first PCI (PCICT). Then, the two observers reviewed MRI examinations in combination with CT and determined a second PCI (PCICT+MRI). The sensitivity and negative predictive value of the two imaging sets were determined using surgery as a reference standard (PCIRef). Results CT plus MRI was more accurate in predicting the surgical PCI than CT alone. The absolute difference between PCICT+MRI and PCIRef was lower than that between PCICT and PCIRef (mean(s.d.) 3·96(4·10) versus 4·89(4·73); P = 0·010). The number of true-positive findings increased from 106 to 125 for reader 1 and from 117 to 132 for reader 2 with the adjunct of MRI. For both readers, an increased sensitivity was obtained when both MRI and CT were used (from 63 to 81 per cent for reader 1; from 44 to 81 per cent for reader 2). The increase in sensitivity was greater for patients with a moderate volume of disease. Conclusion The combination of CT and MRI improved the preoperative estimation of PCI compared with CT alone.
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 | 0.002 | 0.016 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".