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Record W4393931655 · doi:10.1002/mp.16993

In reply to Masjedi et al

2024· editorial· en· W4393931655 on OpenAlexaff
Marco Carlone, Ray Yeng Yang, Derek Hyde, Nathan Becker, John Cocarell

Bibliographic record

VenueMedical Physics · 2024
Typeeditorial
Languageen
FieldMedicine
TopicLung Cancer Research Studies
Canadian institutionsUniversity of British Columbia, Okanagan CampusKelowna General HospitalUniversity of British Columbia
Fundersnot available
KeywordsPhilosophy

Abstract

fetched live from OpenAlex

We thank Dr. Masjedi for taking the time to review our paper, Measurement of neutron yield for a medical linear accelerator below 10 MV,1 and to provide thoughtful commentary on our experimental methods. The motivation for this work was to address the lack of experimental data in the literature regarding neutron doses from medical linear accelerators at intermediate energies, between 6 and 10 MV. Medical linear accelerators typically use a 5.5 MeV electron beam to produce a 6 MV beam. Since this is below the activation threshold for most common elements (see Figure 5 in our paper1), and since medical linear accelerator OEMs do not include other energies below 10 MV in commercially available systems, there was not any data available in the literature at any energy below 10 MV for a commercially available medical linear accelerator. We believe this to be an important area where more data will benefit the radiotherapy community since it may enable and help to justify technique development for volume-based radiotherapy treatments at intermediate energies. So, the primary purpose of the experiment we reported on was simply to provide first estimates of photoneutron yields at an intermediate energy of 8 MV. We acknowledge that the experimental methodology can be improved, and we hope that other investigators will undertake further experiments to increase the availability in the open literature of photoneutron yield estimates in this energy range. Regarding the measurements with jaws open and closed, we reviewed our original data books, and confirm that the values reported in Table S1 are what was recorded in our logbooks. However, we did not rely on this data for any of the analysis. It appears, as Dr. Masjedi suggests, to be possibly related to photon contamination from the open field, and we thank Dr. Masjedi for bringing this to our attention. The results presented in Table S1 are the complete set of measurements that we obtained and are presented with the paper in the case where they may have value to a reader. We only relied on the data presented in Table 3 for our analysis, which were all taken with jaws closed, where the issues with dose pile up should not be a concern. We also agree that the presence of the flattening filter in the experiment was not ideal and adds uncertainty to the result. This was acknowledged in Section 4.6 of the paper, paragraph 1. While we did not discuss this directly in the write up of the paper, we admit that we had not fully anticipated the effects of the flattening filter on the experimental results until we were conducting the experiment. By this time, we were not able to respond ideally since the timeline of the experiment was largely dictated by the machine replacement schedule, which was strictly controlled. We hope to be able to repeat these measurements in the future, and we will have a better plan for understanding the effects of the flattening filter. We also encourage other investigators to repeat these experiments under better controlled conditions, to improve the accuracy of the neutron dose associated with intermediate energy photon beams. In doing these experiments, a significant and limiting factor was the timeline dictated by the linear accelerator replacement cycle. Ideally, we would have had time to collect data, and adapt our experimental methods accordingly. As well, since our clinic is not equipped with the ideal neutron detecting devices, we spent some time optimizing the measurements to see what conditions would provide optimal data. So, for this reason, we tried several room configurations, beam directions, jaw positions, and even measurement points in the maze of the linear accelerator bunker. The data presented in Table S1 is meant to provide as much transparency over our experimental methodology, and in this spirit, we welcome and encourage commentary on our methodology as we believe this will instill confidence in the results. In general, we appreciate the comments from Dr. Masjedi, and agree with the sentiment of the comments, which was that the experimental methodology of the neutron measurements can be improved. We did attempt to place uncertainties on the measurements by incorporating uncertainty in the neutron spectrum into our final results, which are larger than the 25% variation in data discussed by the reader. The difficulty with these types of experiments is that they are done in a non-ideal environment (a hospital, with limited access to neutron detection equipment), and under strict time constraints. This is why, in Section 4.4, where we present the principal results of our experiment, we quote the neutron source strength from an electron beam at 8 MeV to be “about 1 order of magnitude less than 10 MV photon beam”. By using the term order of magnitude, our intent was to convey the uncertainty in our result. However, the comments brought up in Dr. Masjedi's letter are valid, and we agree that a specific error analysis is a better approach to explain the uncertainty in our experimental results. Finally, we note that for other reports of photoneutron yield, significant variation in results exist depending on the type of medical linear accelerator used in the study (see for instance, Table 2 of Naseri and Mesbahi,2 and Figure 4 of Carlone and colleagues1). This is presumably due to the inherent difficulties of conducting this type of experiment in a hospital environment, and the variation in composition and construction of different medical linear accelerators. We encourage potential investigators who may be contemplating the types of measurements we describe in our paper to utilize the most accurate experimental methodology, as suggested by Dr. Masjedi, to reduce uncertainties in this general area, and improve the quality of data available.

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)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.138
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0000.001

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 head0.020
GPT teacher head0.417
Teacher spread0.397 · 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 designNot applicable
Domainnot available
GenreEditorial

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

Citations0
Published2024
Admission routes1
Has abstractyes

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