Bibliographic record
Abstract
The authors 1 report having performed a secondary analysis on the data in Honey et al. 2 Based on these data, the authors concluded that there is a specific access problem to deep brain stimulation (DBS) in rural areas of the Atlantic provinces.They were interested how centralization of DBS services may impact people living in rural areas.This letter echoes some interesting and valuable thoughts that might help to improve an equal access to complex neuromodulation procedures such as DBS or even magnetic resonance-guided focused ultrasound surgery (MRGFUS).Even though I agree it is crucial to address access issues in remote and rural areas, the conclusions of this letter are not necessarily substantiated due to data that are actually missing in the primary data source.First of all, the primary article does not include the site where the surgery was performed.The method section states "No data were provided on gender, diagnosis, wait time for surgery, implantation hospital or surgeon, electrode target or clinical outcome" as the data were retrieved from the industry and not from the implanting sites.Furthermore, the authors claim significant access issues but do not provide any information on which statistical test was used to prove significance.This is in contrast to the original article that had already analyzed the access between rural areas and the entire provincial populations: "Within each province, the percentage of patients receiving DBS who lived in a rural area was calculated and compared with the percentage of all people living in a rural area within that province.There was no significant difference between the percentage of patients receiving DBS from rural areas".The authors of the letter do not explain why their secondary analysis came to a different conclusion.The graphical analysis shows the data in cases per million, which is difficult when talking about rural communities in the Atlantic provinces, that comprise a population between several 10,000 to a maximum of 300,000 or 400,000.Therefore, small changes in small population lead to large differences when scaling them up to a million.This makes these numbers seem significant, which they are not according to Honey et al.In this context, it is of interest that there was a specific access problem for the Atlantic provinces during the study period of 2015-2016.This was a period with a longer hiatus of DBS surgeries in Halifax, which is the main DBS center for this largely rural region.The two neurosurgeons performing DBS had moved out of province or out of country, just before and during the study period.A regular DBS practice in Halifax was restarted by November 2016.In the meantime, surgeries, including the Nova Scotia cases had to be referred to other centers (e.g. in Ontario and Quebec) or had to wait until the program was restarted.This could be an explanation for a certain disparity between the Atlantic and other provinces during the study period.
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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 | 0.005 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.043 | 0.032 |
| Insufficient payload (model declined to judge) | 0.014 | 0.016 |
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".