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Record W3093468569 · doi:10.1016/j.phro.2020.09.012

Magnitude and dosimetric impact of inter-fractional positional variations of the metal port of tissue expanders in postmastectomy patients treated with radiation

2020· article· en· W3093468569 on OpenAlexafffund
Keren Mayorov, Elsayed Ali

Bibliographic record

VenuePhysics and Imaging in Radiation Oncology · 2020
Typearticle
Languageen
FieldMedicine
TopicBreast Implant and Reconstruction
Canadian institutionsOttawa HospitalCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTomotherapyContouringNuclear medicineMedicinePort (circuit theory)Standard deviationRadiation therapySurgeryMathematicsStatisticsComputer science

Abstract

fetched live from OpenAlex

BACKGROUND AND PURPOSE: Postmastectomy breast reconstruction involves the insertion of a temporary tissue expander, which contains a metal injection port. The purpose of this study was to determine the magnitude and dosimetric impact of the inter-fractional positional variations of the port for patients treated with radiation. MATERIALS AND METHODS: For nine breast cases treated on Tomotherapy, the deviation of the port in the daily MVCT from its reference position was measured in the three cardinal directions. The dosimetric effects of the measured errors were evaluated for two classes of error: Internal Port Error (IPE) and Patient Registration Error (PRE). For each class, dose accumulation was done for daily measured errors and a systematic error. RESULTS: Inter-fractional positional errors of the port were small, with 87% of the deviations below 5 mm, but errors larger than 1.5 cm were observed. The cumulative effect of the daily measured and systematic IPE decreased target coverage by as much as 2.8% and 3.5%, respectively. The cumulative effect of the daily measured PRE decreased target coverage by an average of 3.5%. The cumulative effect of a systematic PRE significantly decreased target coverage by an average of 16%. CONCLUSION: The presence of IPE over the course of treatment had minimal clinical impact while PRE had a greater impact on clinically-relevant regions. The robustness of treatment delivery can be improved by assigning the port its appropriate density during planning despite contouring uncertainties due to metal artefacts, and by prioritizing anatomical alignment over port alignment during daily registration.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.190

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.009
GPT teacher head0.283
Teacher spread0.274 · 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

Citations5
Published2020
Admission routes2
Has abstractyes

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