Reflections on: Sphenopalatine ganglion block in patients with post-dural puncture headache
Bibliographic record
Abstract
Dear Editor, I read with great interest the recently published research article on the efficacy of the sphenopalatine ganglion (SPG) block in patients with post-dural puncture headache (PDPH).[1] I congratulate Gayathri et al.[1] for this wonderful study and wish to present my insights on that article. Gayathri et al.[1] concluded that the SPG block “greatly reduced” the requirement for an epidural blood patch (EBP). However, only one patient out of 20 patients in the control group required an EBP, while no patient in the SPG block group required it. This I believe, is neither clinically nor statistically a “great reduction”. Gayathri et al.[1] stated in the “Discussion” section that Akin et al.[2] (cited as reference #16 in the study by Gayathri et al.[1]) in their retrospective study observed that there was a significant reduction of pain in patients who had SPG block upon analyzing it on 26 non-obstetric patients. However, it was only a case report of only one patient, and only a bilateral greater occipital nerve block was used, and not an SPG block. Besides, Akin et al.[2] did not use the patient global impression of change (PGIC) scale as mentioned by Gayathri et al.[1] Regarding the advantages of ropivacaine over bupivacaine, Gayathri et al.[1] stated in the “Discussion” section that the cardiovascular and central nervous system effects are minimal with ropivacaine and cited a total of 4 references for that (References #15, 18–20 of Gayathri et al.[1]). While there is no dispute about that, one of the references cited (Reference #18 of Gayathri et al.[1]) is not in accordance with that statement. Jespersen et al.[3] used the mixture of a1:1 solution of 4% lidocaine plus 0.5% ropivacaine and compared it with a placebo (saline). The main point to note from their study is that they observed that the efficacy of SPG block in avoiding the EBP was similar between the 2 groups; hence, not attributable to any local anesthetics.[3] Lastly, there were some violations of the “Vancouver style” for citation of references in that published study.[1] For instance, in the “Discussion” section, reference #17, 18 were cited without citing references #15, 16 anywhere in the text before this. Again, reference #16 was cited after the citation of reference #18, 19, 20. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.
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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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.016 | 0.017 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".