In Reply: A New Trend of Blended Learning in Neurosurgical Training: Fellowship of Neuroendoscopy
Bibliographic record
Abstract
To the Editor: We read the reply letter to the editor by Rahman et al entitled “In Reply: A New Trend of Blended Learning in Neurosurgical Training: Fellowship of Neuroendoscopy” with great interest.1-3 The authors argue that modern simulation-based training systems are not an ultimate solution for multiple drawbacks, including limited applications for some systems and difficulty or high cost for others. In a conflicting elaboration, they invited policymakers to “start manufacturing these simulators in their own countries to decrease the cost.” We would agree that the cost may be high, especially for low-income countries, and the manufacturers' funding support or special pricing would be a great gesture. However, the call for manufacturing those simulators locally may be ambitious and more of a long-term solution. Establishing a good infrastructure and interested market to attract big industry names is not easy. The authors did not suggest any alternative; it is known that regular cadaveric workshops are costly, have a limited capacity per session, are not reusable, and may not even be an option in many less fortunate countries. We conclude that simulation-based training systems are a good option in the absence of short-term realistic alternatives.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.043 | 0.041 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".