A comparative analysis of longitudinal computed tomography and histopathology for evaluating the potential of mesenchymal stem cells in mitigating radiation-induced pulmonary fibrosis
Bibliographic record
Abstract
Radiation-induced pulmonary fibrosis (RIPF) is a debilitating side effect that occurs in up to 30% of thoracic irradiations in breast and lung cancer patients. RIPF remains a major limiting factor to dose escalation and an obstacle to applying more promising new treatments for cancer cure. Limited treatment options are available to mitigate RIPF once it occurs, but recently, mesenchymal stem cells (MSCs) and a drug treatment stimulating endogenous stem cells (GM-CSF) have been investigated for their potential in preventing this disease onset. In a pre-clinical rat model, we contrasted the application of longitudinal computed tomography (CT) imaging and classical histopathology to quantify RIPF and to evaluate the potential of MSCs in mitigating RIPF. Our results on histology demonstrate promises when MSCs are injected endotracheally (but not intravenously). While our CT analysis highlights the potential of GM-CSF treatment. Advantages and limitations of both analytical methods are contrasted in the context of RIPF.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".