Eliminating the need for multiple injections during a dental procedure; A novel study identifying the greater palatine foramen and nerve using ultrasound
Bibliographic record
Abstract
Background A greater palatine nerve (GPN) block is required for many dental procedures. Localization of the nerve currently relies on a blind, surface landmark approach, which often requires multiple needle pricks and a large amount of local anesthetic. Ultrasound (US) is a safe, non‐invasive modality and bone appears as a white line on an US. Any disruption in this line may indicate a discontinuity in bone such as a foramen. Hypothesis An US‐guided approach can be used to locate the greater palatine foramen (GPF) and isolate the GPN on the hard palate. Material & Methods In this study, 16 cadaveric hard palates were scanned by a linear probe and an US‐guided injection of India ink was administered into the GPF of all the specimens. Result The hard palate was visible as a white continuous line and an interruption in this line near the maxillary molar teeth was recognized as the GPF in all of the 16 specimens. In 9 out of 16 specimens, traces of India ink were found in the greater palatine canal. Conclusion US can be effectively used to visualize both the GPF and GPN in palates with and without molar teeth and an ultrasound guided GPN block could be considered as an adjunct to dental clinics. These findings may warrant follow‐up testing on patients in the dental clinic. The author is supported by OGS funds. Grant Funding Source : Departmental
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".