Bone Ablation without Thermal or Acoustic Mechanical Injury via a Novel Picosecond Infrared Laser (PIRL)
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: A precise means to cut bone without significant thermal or mechanical injury has thus far remained elusive. A novel non-ionizing ultrafast pulsed picosecond infrared laser (PIRL) may provide the solution. Tissue ablation with the PIRL occurs via a photothermal process with thermal and stress confinement, resulting in efficient material ejection greatly enhanced through front surface spallation photomechanical effects. By comparison, the Er:YAG laser (EYL) ablates via photothermal and cavitation-induced photomechanical effects without thermal or acoustic confinement, leading to significant collateral tissue injury. This study compared PIRL and EYL bone ablation by infrared thermography (IRT), environmental scanning electron microscopy (ESEM), and histology. STUDY DESIGN: Prospective, comparative, ex vivo animal model. SETTING: Optics laboratory. SUBJECTS AND METHODS: Ten circular area defects were ablated in ex vivo chicken humeral cortex using PIRL and EYL at similar average power (~70 mW) under IRT. Following fixation, ESEM and undecalcified light microscopy images were obtained and examined for signs of cellular injury. RESULTS: Peak rise in surface temperature was negligible and lower for PIRL (1.56 °C; 95% CI, 0.762-2.366) compared to EYL ablation (12.99 °C; 95% CI, 12.189-13.792) (P < .001). ESEM and light microscopy demonstrated preserved cortical microstructure following PIRL ablation in contrast to diffuse thermal injury seen with EYL ablation. Microfractures were not observed. CONCLUSION: Ablation of cortical bone using the PIRL generates negligible and significantly less heat than EYL ablation while preserving cortical microstructure. This novel laser has great potential in advancing surgical techniques where precision osseous manipulation is required.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.006 | 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 teacher head, 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".