Risk of a Severe Neurological Complication After Regional Anesthesia Should Be Individualized
Bibliographic record
Abstract
In Response: Dr. Fowler (1) clearly points out the inherent limitations, including heterogeneity and underreporting, in our recently published review of neurological complications after regional anesthesia (2). Our manuscript was not meant to be a “one size fits all” definitive list of complication rates; rather, the intent was to consolidate and illustrate the existing data, however flawed, in summary estimates and confidence intervals that may help practitioners address the frequency of severe neurological complications during discussions of risk associated with regional anesthesia. We purposely excluded epidural hematoma and abscess from our review because these complications have been examined in detail elsewhere (3–6) and are intimately associated with unique risk factors, such as thromboprophylaxis and immunodeficiency. We disagree with Fowler's opinion that temporary neuropathy is of questionable significance during the informed consent process because neuropathy, however transient, may cause considerable distress to patients (and their practitioners) as well as prompt costly consultation and invasive investigation. As a matter of course, complication rates should ideally be “tailored” to the individual patient according to each risk factor that he or she presents. However, the data that would enable us to calculate the true incidence of severe neurological complications associated with regional anesthesia are currently unavailable. Large-scale,multi-institutional, prospective projects, such as the 3rd National Audit of Major Complications of Spinal and Epidural Anaesthesia currently underway in the United Kingdom (7), will hopefully bring us closer to the elusive grand denominator that undermines calculations of incidence for such rare events. Until then, we must continue to analyze, scrutinize, but importantly, use all of the existing data in the best way we can. A concise, user-friendly, consolidated resource for estimating the risk of neurological complications after regional anesthesia seemed to us like a good place to start. Richard Brull, MD, FRCPC Colin J.L. McCartney, MBChB, FRCA, FFARCSI, FRCPC Vincent W.S. Chan, MD, FRCPC Hossam El-Beheiry, MBBCh, PhD, FRCPC Department of Anesthesia and Pain Management Toronto Western Hospital University Health Network University of Toronto Toronto, ON 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 | 0.015 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.010 | 0.020 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".