Cervical Spine Motion: A Fluoroscopic Comparison During Intubation with Lighted Stylet, GlideScope, and Macintosh Laryngoscope
Bibliographic record
Abstract
In Brief The question of which is the optimum technique to intubate the trachea in a patient who may have a cervical(C)-spine injury remains unresolved. We compared, using fluoroscopic video, C-spine motion during intubation for Macintosh 3 blade, GlideScope®, and Intubating Lighted Stylet, popularly known as the Lightwand or Trachlight®. Thirty-six healthy patients were randomized to participate in a crossover trial of either Lightwand or GlideScope to Macintosh laryngoscopy, with in-line stabilization. C-spine motion was examined at the Occiput-C1 junction, C1-2 junction, C2-5 motion segment, and C5-thoracic motion segment during manual ventilation via bag-mask, laryngoscopy, and intubation. Time to intubate was also measured. C-spine motion during bag-mask ventilation was 82% less at the four motion segments studied than during Macintosh laryngoscopy (P < 0.001). C-spine motion using the Lightwand was less than during Macintosh laryngoscopy, averaging 57% less at the four motion segments studied (P < 0.03). There was no significant difference in time to intubate between the Lightwand and the Macintosh blade. C-spine motion was reduced 50% at the C2-5 segment using the GlideScope (P < 0.04) but unchanged at the other segments. Laryngoscopy with GlideScope took 62% longer than with the Macintosh blade (P < 0.01). Thus, the Lightwand (Intubating Lighted Stylet) is associated with reduced C-spine movement during endotracheal intubation compared with the Macintosh laryngoscope. IMPLICATIONS: The question of which is the optimum technique to intubate the trachea of a patient who may have a cervical(C)-spine injury remains unresolved. Using fluoroscopic video, C-spine motion during intubation was compared for Macintosh 3 blade, GlideScope, and Lightwand use. The Lightwand is associated with reduced C-spine movement during intubation compared to Macintosh laryngoscope.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".