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Record W2611412507

Surgical Gloves and Instruments: Potential Vectors of Cancer Cell Seeding

2017· dissertation· en· W2611412507 on OpenAlexaff
David Berger‐Richardson

Bibliographic record

VenueTSpace (University of Toronto) · 2017
Typedissertation
Languageen
FieldMedicine
TopicReconstructive Surgery and Microvascular Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSeedingSurgical GlovesMedicineBiomedical engineeringSurgeryBiologyAgronomy
DOInot available

Abstract

fetched live from OpenAlex

Cancer recurrence in surgical wounds occurs by unknown mechanisms. My hypothesis is that tumour cells are harboured on surgical gloves and instruments during the extirpative phase of a cancer resection and can exfoliate into the wound during reconstruction, with potential for growth. A systematic review revealed that the incidence of port-site metastasis in gallbladder cancer is especially high. A survey of practising surgeons found that there is no consensus as to whether specific intraoperative protective strategies are warranted. An analysis of washings from gloves and instruments used intraoperatively during cancer resections identified malignant cells on approximately half of gloves and instruments that had directly handled malignant tissue (debulking of peritoneal malignancy, incisional biopsy of extremity sarcoma). No malignant cells were identified on a glove or instrument used in margin-negative sarcoma resection. These results suggest that specific protective strategies may not be warranted in curative resections.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.811
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0050.000

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 head0.008
GPT teacher head0.265
Teacher spread0.256 · 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.

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

Citations2
Published2017
Admission routes1
Has abstractyes

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