Evaluation of laser-Doppler perfusion imaging for measurement of blood flow in cortical bone
Bibliographic record
Abstract
Most techniques currently available to measure blood flow in bone are time consuming and require destruction of the tissue, but laser-Doppler technology offers a less invasive method. This study assessed the utility of laser-Doppler perfusion imaging (LDI) to measure perfusion in cortical bone. Twelve mature New Zealand White rabbits were assigned to one of three groups: normal control, constriction (norepinephrine), or dilatation (nitroprusside). The left and right medial tibiae were consecutively scanned at red (634-nm) and near-infrared (810-nm) wavelengths to examine the repeatability of LDI output. The pharmacological intervention groups were injected with the respective drug, and LDI measurements at 810 nm were obtained concurrently with colored microsphere-determined flow in all of the groups. LDI effectively quantified blood flow in cortical bone and detected physiologically induced changes in perfusion. A significant positive correlation was found between microsphere-determined flow and LDI output (r = 0.6, P < 0.05). Repeatability of consecutive LDI measurements was within 5%. The effectiveness of LDI to measure perfusion in bone suggests this method has potential for investigating the role of blood flow in bone metabolism and remodeling.
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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 | 0.000 | 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".