Anatomical Detail and Accuracy of the Pernkopf Atlas and Examples of Clinical Impact
Bibliographic record
Abstract
The high fidelity anatomical structural detail seen in the Pernkopf atlas remains unmatched in other references, including surgical anatomy atlases. An example of serial dissection illustrations are examined herein, in relation to an anatomically based clinical question. The question is about radiofrequency nerve ablation, an image-guided procedure that provides a non-opioid alternative to treat joint pain. To perform these image-guided procedures effectively, the location and course of the nerve(s) being targeted is very important. Although the patient had good pain relief, the clinician was concerned about the patient's loss of sensation around the anus following an ablation procedure of the nerves innervating the sacroiliac joint, and asked for more information about the clunial nerves and their relevance to this procedure. The anatomical illustrations in the Pernkopf atlas are highly detailed and drawn from serially dissected specimens from the skin superficially to the level of the origin of the nerves from the vertebral column deeply. Tracing the clunial nerves through five serial illustrations provided the necessary anatomical insight required to answer this clinical question for development of the ablation procedure. This atlas could play a significant role in educating future clinicians and surgeons and provide answers to anatomically related clinical quandaries. However, the atlas must always be used by first acknowledging its origins and history.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".