Bibliographic record
Abstract
We are grateful to van Zundert et al. for their interest in our paper, and acknowledge their contribution to our methodology [1]. There are, however, significant differences between their studies and ours that precluded using their findings for sample size calculations. Lee et al. measured the point force applied to a hard surface, the maxillary incisors, during laryngoscopy by attaching the sensors to the convex (dorsal) surface of the laryngoscope blades, close to the handle [2]. In contrast, we applied the force sensors to the concave (ventral) surface of the blades, measuring the forces applied by the laryngoscope to the base of the tongue, whilst trying to avoid making contact with the maxillary teeth. Their subsequent report was published after our article had been submitted but it was unsuitable for the same reasons [3]. As described in our paper, we modified the sensors in consultation with the manufacturer to reflect more accurately the force applied over a soft surface. These raised pucks may have increased the ‘point’ load measured by our sensors, but they were applied in an identical manner using both the Macintosh and GlideScope blades and we believe that comparisons of the applied force to the tongue base are valid. The sensors did not cover the entire blade surface so may have failed to measure applied forces beyond the sensing area (though unpublished preliminary studies on manikins demonstrate that such forces are negligible). We agree that a wider examination of the force applied using different types of videolaryngoscopes may be interesting, but this was not the focus of our study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".