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Record W3008741759 · doi:10.1503/cjs.001119

Implementing new surgical technology: a national perspective on case volume requirement for proficiency in transanal total mesorectal excision

2020· article· en· W3008741759 on OpenAlexafffundvenueabout
Vanessa N. Palter, Sandra L. de Montbrun

Bibliographic record

VenueCanadian Journal of Surgery · 2020
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Surgical Treatments
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
FundersUniversity of Toronto
KeywordsMedicineTotal mesorectal excisionInterquartile rangeColorectal cancerGeneral surgerySurgeryCancerInternal medicine

Abstract

fetched live from OpenAlex

Background: Early data suggest that transanal total mesorectal excision (TaTME) is a safe alternative to the abdominal approach for rectal cancer. This study aims to understand the approach to the management of rectal cancer in Canada and to ascertain perspectives on introducing TaTME. Methods: Surgeons were invited to complete a survey that asked about their management practices relating to rectal cancer and their opinions regarding TaTME. Results: Ninety-four surgeons completed the survey (38% response rate). The number of rectal cancer cases handled annually by surgeons varied widely (1–80 cases, median 15 cases). Twenty-seven percent of respondents performed TaTME at the time of the survey, and 43% of those who did not said they planned on learning the technique. Surgeons who performed TaTME felt that a higher annual volume of rectal cancer cases was required to maintain proficiency than did non-TaTME surgeons (median 20 cases [interquartile range (IQR) 15–25 cases] v. 15 cases [IQR 10–20 cases]). Surgeons who performed TaTME also felt that a higher annual volume of TaTME cases was required to maintain proficiency (median 12 cases [IQR 10–19 cases] v. 9 cases [IQR 5–10 cases]). Conclusion: These findings help define the current practice environment for rectal cancer surgeons in Canada and highlight the complex issues associated with learning TaTME.

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 score0.189
Threshold uncertainty score0.778

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.088
GPT teacher head0.342
Teacher spread0.254 · 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

Citations8
Published2020
Admission routes4
Has abstractyes

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