Bibliographic record
Abstract
Over the centuries, discoveries of lands unknown, treasures lost and buried, and formulas to delineate physicochemical processes have led to advancements in our understanding of how the world is structured and governed. In science and medicine, discoveries are frequently made following deliberate periods of observation and experimentation to test hypotheses. However, in some instances, discoveries may arise either following a "eureka moment" that transcends rigorous scientific experimentation or following a serendipitous observation. In many instances, scientific discoveries will lead to new inventions that are aimed at improving the manner in which tasks or operations are performed. In this address, some of the key discoveries in science and medicine that have impacted significantly on the field of neurosurgery are described. Some of these include discoveries in neuroanatomy, anesthesiology, infectious diseases, antisepsis, and radiology. Discoveries in the field of molecular science, from the discovery of DNA to next-generation DNA sequencing, which have helped improve the diagnosis and prognosis of neurosurgical patients with conditions such as brain tumors, are also described. In the end, these discoveries have led us to new frontiers in the subspecialty practice of neurosurgery. Navigating our way through these new frontiers will undoubtedly lead to additional discoveries that are unimaginable at present but bound to improve the future care of neurosurgical patients.
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 | 0.018 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.010 | 0.025 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".