Bibliographic record
Abstract
To the Editor: We read with interest the article by Evron et al. (1). Unfortunately, there are some ambiguities in the tables that cast doubt on the main conclusions. In particular, it is not possible, in Table 2 of their article, to determine how the P values were derived. Using a two-tailed Fisher's exact test and comparing the “air” to the “lidocaine” groups the P value for the incidence of accidental dural puncture is 0.2, not <0.02. If the two lidocaine groups are combined, the P value becomes 0.035, favoring the combined group. This does not make clinical sense, since the needle in the “air + lidocaine” group is placed using loss of resistance to air. Also, the P value for the incidence of unblocked segments is 0.06 when the “air” group is compared to the “lidocaine” group and 0.03 when air is compared to the “ air +lidocaine” group. While the latter value is statistically significant, it may be due to either the increase in volume of 2% lidocaine in the “air + lidocaine” group (3 vs 6 mL), or statistical error (multiple testing). When reporting P values, it is important to state exactly how they were derived in order to aid in interpretation. Stephen Halpern, MD Pamela Angle, MD Department of Anaesthesia Sunnybrook and Women's College Health Sciences Centre Toronto, Canada [email protected]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.014 | 0.018 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".