The use of CT perfusion to monitor the effect of hypocapnia during laser thermal therapy in a rabbit model
Bibliographic record
Abstract
One of the limiting factors in the thermal therapy of tumours is the dissipation of heat by blood flow. The current study investigates the use of hyperventilation (hypocapnia) to decrease tumour blood flow during laser thermal therapy. Rabbits with implanted VX2 thigh tumours were treated using a diode laser (805 nm) as the heating source. One group of rabbits (n = 8) was treated with the new hypocapnia protocol and another group (n = 8) with the conventional (normocapnia) protocol. The mean tumour volume and blood flow were the same for the two groups prior to treatment. The laser power temporal profile (3.0-1.5 W) and the duration of treatment (60 min) were also the same for both treatment protocols. Blood flow maps were calculated from a series of contrast-enhanced CT images. The average change in thermal lesion area at 60 min post-laser thermal therapy from pre-treatment normalized to the pre-treatment tumour area was significantly different between the two treatment protocols: 0.52+/-0.13 (hypocapnia) vs 0.33+/-0.12 (normocapnia) (p < 0.001). Similarly, the average fractional decrease in global tumour blood flow 60 min post-treatment from pre-treatment was also significantly different between the two protocols: 0.64+/-0.10 (hypocapnia) vs 0.41+/-0.14 (normocapnia) (p < 0.001). The hypocapnia protocol produced larger thermal lesion area and greater decrease in tumour blood flow post-treatment than the normocapnia protocol. These results support the further investigation of the use of hypocapnia to increase the therapeutic effect of laser thermal therapy.
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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) | 0.001 | 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 | 0.001 |
| 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".