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Record W3169525295 · doi:10.7759/cureus.15598

Implementation of Single-Fraction Lung Stereotactic Ablative Radiotherapy in a Multicenter Provincial Cancer Program During the COVID-19 Pandemic

2021· article· en· W3169525295 on OpenAlexaffabout
Benjamin Mou, Derek Hyde, Cynthia Araujo, Leigh Bartha, Alanah Bergman, Mitchell Liu

Bibliographic record

VenueCureus · 2021
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Diagnosis and Treatment
Canadian institutionsCanadian Association of Nurses in OncologyKelowna General HospitalBC Cancer Agency
Fundersnot available
KeywordsSABR volatility modelMedicineRadiation therapyLung cancerPandemicMedical physicsCoronavirus disease 2019 (COVID-19)Family medicineInternal medicineDisease

Abstract

fetched live from OpenAlex

Background During the novel coronavirus disease 2019 (COVID-19) pandemic, cancer centers considered shortened courses of radiotherapy to minimize the risk of infectious exposure of patients and staff members. Amidst a pandemic, the process of implementing new treatment approaches can be particularly challenging in larger institutions with multiple treatment centers. We describe the implementation of single-fraction (SF) lung stereotactic ablative radiotherapy (SABR) in a multicenter provincial cancer program. Materials and Methods British Columbia, Canada has a provincial cancer program with six geographically distributed radiotherapy centers serving a population of 5.1 million, over 944,735 square kilometers. In March 2020, provincial mitigation strategies were developed in case of reduced access to radiotherapy due to the COVID-19 pandemic. SF lung SABR was identified by the provincial lung radiation oncology group as a mitigation measure supported by high-quality randomized evidence that could provide comparable outcomes and toxicity to existing fractionated SABR protocols. A working group consisting of radiation oncologists and medical physicists reviewed the medical literature and drafted consensus guidelines that were reviewed by a group of center representatives as a component of provincial lung radiotherapy mitigation strategic planning. Individual centers were encouraged to implement SF lung SABR as their resources and staffing would allow. Centers were then surveyed about barriers to implementation. Results On March 24, 2020, a working group was created and consensus guidelines for SF lung SABR were drafted. The final version was approved and distributed by the working group on March 26, 2020. The provincial lung radiotherapy mitigation strategy group adopted the guidelines for implementation on April 1, 2020. Implementation was completed at the first center on April 27, 2020. Barriers to implementation were identified at five of six centers. Two centers in regions with disproportionately high COVID-19 cases described inadequate staffing as a barrier to implementation. One center encountered delays due to pre-scheduled commissioning of new treatment techniques. Three centers cited competing priorities as reasons for delay. As of May 2021, two centers had active SF lung SABR programs in place, three centers were in the process of implementation, and one center had no immediate plans for implementation due to ongoing resource issues. Conclusion SF lung SABR was adopted by a provincial cancer program within weeks of conception through rapid communication during the development of COVID-19 pandemic mitigation strategies for radiotherapy. Although consensus guidelines were written and approved in an expedited timeframe, the completion of implementation by individual centers was variable due to differences in resource allocation and staffing among the centers. Strong organizational structures and early identification of potential barriers may improve the efficiency of implementing new treatment initiatives in large multicenter radiotherapy programs.

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 score0.015
Threshold uncertainty score0.999

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 head0.039
GPT teacher head0.405
Teacher spread0.366 · 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
Published2021
Admission routes2
Has abstractyes

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