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Record W2582226582 · doi:10.3171/2016.9.jns161119

History of neurosurgery at University of Toronto: the St. Michael's story

2017· article· en· W2582226582 on OpenAlexaffabout
Naif M. Alotaibi, Daipayan Guha, Christopher S. Ahuja, Julian Spears, Paul J. Müller, William S. Tucker, Alan R. Hudson, R. Loch Macdonald

Bibliographic record

VenueJournal of neurosurgery · 2017
Typearticle
Languageen
FieldMedicine
TopicHistory of Medical Practice
Canadian institutionsSt. Michael's HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineNeurosurgeryInstitutionLibrary scienceClassicsGerontologySurgeryHistorySociologySocial science

Abstract

fetched live from OpenAlex

In this paper, the authors describe the history of neurosurgery at St. Michael's Hospital, University of Toronto. St. Michael's has long been regarded as one of the top teaching and research hospitals in Canada. A detailed literature review of published and unpublished works was performed to formulate a succinct but in-depth review of its development, successes, and challenges. This fascinating 125-year history serves as a reminder of the importance of their institution's origins, and the authors hope that it will be a useful guide for developing programs around the world.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.989
Threshold uncertainty score0.787

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0110.010
Scholarly communication0.0070.004
Open science0.0010.003
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0100.002

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.

Opus teacher head0.038
GPT teacher head0.261
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2017
Admission routes2
Has abstractyes

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