P.140 A new international nomenclature of far and extreme lateral approaches to the craniocervical junction
Bibliographic record
Abstract
Background: Far and extreme lateral approaches have become a mainstay treatment for lesions located at the anterolateral aspect of foramen magnum and its vicinity. However, there is a significant discrepancy between authors on what these approaches truly are, which leads to producing papers naming different techniques the same and same techniques differently. Methods: We performed literature search employing PubMed-MEDLINE and Scopus databases. The search terms referred to the nomenclature of far lateral approach (FLA), extreme lateral approach (ELA), and their variants. Finally, important papers on the topic from article references were also included, if deemed contributory. Results: In total, 37 articles were collected. Surprisingly, we found that not a single paper has addressed the confusing nomenclature directly yet. Nine truly separate variants of FLA and ELA were found. We implemented them intraoperatively depending on both patient and lesion characteristics. The essence about each is summarized. Conclusions: In the CNSF meeting, we will shortly discuss causes behind confusion and debate each FLA and ELA variants according to a number of authors and their unique yet sometimes confusing understanding of the approaches. Ultimately, a logical proposal for the unification is provided to stir up discussion
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".