Cervical nerve root blocks for chronic cervical radiculopathy - Does it influence surgical decision making?
Bibliographic record
Abstract
Objective: It is often difficult to pinpoint the affected nerve root/roots from clinical symptoms and Magnetic Resonance Imaging (MRI) alone in patients with chronic cervical radiculopathy and multilevel degenerative changes. MRI often shows degenerativechanges at more than one level. Degenerative changes can occur in patients without symptoms and clinical diagnosis. Analysesof referred pain distribution from cervical nerve roots have shown only 50% correlation to the classical sensory dermatome. Surgical treatment of patients with cervical radiculopathy attributed to degenerative disease is associated with moderate outcomeresults. Our aim was to assess the diagnostic value of cervical selective nerve root blocks (SNRB) in our Trust in surgical decisionmaking. Methods: The data was collected retrospectively from electronic hospital records on CRIS, PACS and NOTIS on consecutivepatients who underwent cervical nerve root blocks for diagnostic purpose between 1st Jan 2011 and 31st December 2011. Results: Total of 50 patients had cervical SNRB for diagnostic reasons. It influenced surgical decision making in 84% (42) ofthese patients and not in 2% cases. 10% did not have any follow up after cervical SNRB. Decision in favour of surgery wasmade in 71.5% of these 42 patients. Conclusions: In chronic cervical brachialgia, cervical SNRB is extremely influential in surgical decision making, in bothwhether to operate and which levels scenario.
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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 | 0.003 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".