Parents who refuse vitamin K for newborns are also likely to refuse vaccinations, Canadian study finds
Bibliographic record
Abstract
Background and aims Epidural injections of corticosteroids are commonly used as a nonsurgical treatment for radicular pain. Preprocedure ultrasound neuraxial has been associated with reduced risk of location failure of the epidural space. A handheld ultrasound device (Accuro, Rivanna Medical) recognizes lumbar spine bony landmarks and offering automated real-time identification of interspaces and epidural depth. The aim of this study was to evaluate the accuracy of epidural depth estimation of an ultrasound device. Methods 14 patients with lumbar radiculopathy, but without significant degenerative changes or anatomical abnormalities, were included in our prospective study. After the sonographic location, the device allowed to place a mark where the puncture was performed and provided an estimated distance to the epidural space. A transforaminal approach was performed through loss of resistance technique. Later, a mixture of LA and nonparticulated steroids was injected. Results The mean age was 64.21 years (SD 6.39) and BMI was 29.36 (SD 3.52). The mean needle distance was 5.32 ± 0.69 cm (4.0–6.8 cm), the mean estimated depth for divide was 4.07 ± 0.53 cm (3.0–5.2 cm), with a mean difference of -1.25 cm (SD 0.67 cm). All the epidural spaces were located at the point marked; only 5/14 patients needed a minimum redirection of the needle. Time was less than 5 minutes in all patients and the mean was 171.29 sec (SD 43.94). There were no complications. Conclusions The use of handheld ultrasound Accuro underestimates the distance skin-epidural space but helps the location of space, quickly and with few movements of the needle.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".