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Record W2740802591 · doi:10.1503/cjs.007617

Toward late career transitioning: a proposal for academic surgeons

2017· article· en· W2740802591 on OpenAlexafffundvenueabout
Robin R. Richards, Robin S. McLeod, David A. Latter, Shaf Keshavjee, Ori D. Rotstein, Michael G. Fehlings, Najma Ahmed, Avery B. Nathens, James T. Rutka

Bibliographic record

VenueCanadian Journal of Surgery · 2017
Typearticle
Languageen
FieldMedicine
TopicAortic aneurysm repair treatments
Canadian institutionsUniversity of Toronto
FundersUniversity of TorontoPfizer
KeywordsMedicineConstructivePlan (archaeology)Career developmentAcademic institutionMedical educationInstitutionProcess (computing)NursingManagementSociology

Abstract

fetched live from OpenAlex

SUMMARY: In the absence of a defined retirement age, academic surgeons need to develop plans for transition as they approach the end of their academic surgical careers. The development of a plan for late career transition represents an opportunity for departments of surgery across Canada to initiate a constructive process in cooperation with the key stakeholders in the hospital or institution. The goal of the process is to develop an individual plan for each faculty member that is agreeable to the academic surgeon; informs the surgical leadership; and allows the late career surgeon, the hospital, the division and the department to make plans for the future. In this commentary, the literature on the science of aging is reviewed as it pertains to surgeons, and guidelines for late career transition planning are shared. It is hoped that these guidelines will be of some value to academic programs and surgeons across the country as late career transition models are developed and adopted.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.099
GPT teacher head0.301
Teacher spread0.202 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations16
Published2017
Admission routes4
Has abstractyes

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