Bibliographic record
Abstract
Contrast Spread Technique (CST) is a new and evolving method for epidural space recognition. It is based on the interpretation of the radiological images and possesses some theoretical advantages over the conventional loss of resistance (LOR) technique. Unlike the LOR technique, which relies on the subjective feeling of the performing physician, the CST technique allows for objective verification of the needle tip location inside or outside of the epidural space by visual assessment of the contrast spread that may also be observed and interpreted by the third party. By putting the emphasis on the analysis of resulted radiological images instead of relying on the tactile sense of resistance, it may improve the accuracy of the needle placement and improve the safety of the epidural injections by preventing dural penetration. I safely performed more than 1500 injections with CST and, together with my coworkers, created an algorithm for performing cervical ESI with this technique. I also performed an IRB approved study (Canadian SHIELD, 07/18/19) where both techniques were compared. Cervical ESI was performed with either 18G or 25G needle, with 20 patients in each group. In both groups, 95% Confidence Interval for the proportion of epidural space detection was significantly less for LORT. There was also a significant difference between the proportions of detection of epidural space confirmed by LORT using 18G needle and 25G needles: 60% vs. 10%. Epidural space recognition was 100% for CST in both groups. Discussion & Conclusion: In both groups, CST was superior to LORT in epidural space recognition. Although it is understandable for 25G group, it is unclear why in 18G group visual recognition of the contrast spread came before the tactile loss of resistance. Further studies are warranted to explore a new technique.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".