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Record W3090389115 · doi:10.3171/2020.7.focus20447

Ethical issues in geriatric cranial neurosurgery

2020· article· en· W3090389115 on OpenAlexaff
Laureen D. Hachem, Mark Bernstein

Bibliographic record

VenueNeurosurgical FOCUS · 2020
Typearticle
Languageen
FieldMedicine
TopicNeurosurgical Procedures and Complications
Canadian institutionsToronto Western HospitalUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsNeurosurgeryGeriatric careMedicineEthical issuesNeuroethicsPsychologyEngineering ethicsNursingPsychiatryEngineering

Abstract

fetched live from OpenAlex

The global demographic shift to an older population has led to the emergence of the new field of geriatric neurosurgery. Beyond the complexities of disease states and multimorbidity, advanced age brings with it intricate ethical issues pertaining to both the practice and provision of medical and surgical care. In this paper, the authors describe the central ethical themes seen across the spectrum of common neurosurgical conditions in the elderly and highlight the use of foundational ethical principles to help guide treatment decision-making.

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)0.078
metaresearch head score (Gemma)0.117
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.078
Threshold uncertainty score0.410

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0780.117
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0160.050
Scholarly communication0.0100.006
Open science0.0020.009
Research integrity0.0160.018
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.302
Teacher spread0.270 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations14
Published2020
Admission routes1
Has abstractyes

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