Response to Canadian Neurophysiology Laboratory COVID-19 Practice Guidelines
Bibliographic record
Abstract
Response to Canadian Neurophysiology Laboratory COVID-19 Practice Guidelines I was pleased to see CJNS publish "Practice guidelines for Canadian neurophysiology laboratories during the COVID-19 pandemic," a consensus guideline from the Canadian Society of Clinical Neurophysiologists. 1 As the director of a neurophysiology laboratory, such guidelines are highly valuable in decision-making.While for the most part I agree with the recommendations, there are certain aspects that I feel bear review.The authors did not include a Methods section, which is a standard practice in preparing clinical practice guidelines. 2 When considering whether to follow a given guideline, a key factor is the rigor demonstrated in the literature review and process to reach consensus; however, in this case the authors only state in the Purpose that "Recommendations are based on expert opinion and review of relevant published guidelines."One of the primary aims of the guidelines is reducing the risk of COVID-19 transmission to neurophysiology laboratory staff members, yet the author list does not note anyone with expertise in public health or infectious disease.If the authors had reached out for input from other specialties such as these, the guidelines would carry more weight.
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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.027 | 0.014 |
| Insufficient payload (model declined to judge) | 0.042 | 0.018 |
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".