Bibliographic record
Abstract
In their article on perceived benefits and barriers to a career in neurosurgery,1 Dias and colleagues present timely and important research relevant to the future of our field. It also forces readers to frame their own longago decisions in the context of its findings. Why did we choose pediatric neurosurgery (PNS)? Who did we identify with early on? What influenced our own decision? How do we reflect our field to others? Can we do this better individually? As a subspecialty? Nearly 500 US and Canadian neurosurgical residents were surveyed in 2008–2009 via an online survey designed by the authors. Basic demographics were obtained as well as information about the educational environment in medical school and residency in terms of pediatric neurosurgery exposure and mentors. The meat of this paper for our specialty is served when the career plans are discussed, particularly the perceptions about how 40 vetted factors might influence the decision to choose pediatric neurosurgery subspecialty training.1 The article has much to consider, but these comments will concentrate on the 2 most relevant aspects in my opinion: perceptions and mentoring.
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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.015 | 0.018 |
| Insufficient payload (model declined to judge) | 0.016 | 0.014 |
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".