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Record W3121678103 · doi:10.1093/neuonc/noaa305

Role of surgery for glioblastoma: response to letters from Dr. Gerritsen and his colleagues and Dr. Vargas Lopez

2020· letter· en· W3121678103 on OpenAlexaff
Patrick Y. Wen, Michael Weller, E. Antonio Chiocca, Michael Lim, Joerg‐Christian Tonn, Gelareh Zadeh, Kenneth Aldape, Martin J. van den Bent

Bibliographic record

VenueNeuro-Oncology · 2020
Typeletter
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsGlioblastomaMedicinePsychologyPsychoanalysisCancer research

Abstract

fetched live from OpenAlex

We thank Dr. Gerritsen and his colleagues and Dr. Vargas Lopez for their comments regarding the role of surgery for patients with glioblastoma, with reference to our consensus review article appearing in this journal.1 We agree with Dr. Gerritsen and his colleagues regarding the importance of maximizing the extent of resection while minimizing the risk of neurological morbidity. They propose a novel grading scale to translate these surgical goals into a merged “onco-functional clinical outcome.” Such an instrument combining assessment of the extent of resection with one evaluating functional outcome or both quality of life and neurologic function would potentially be an important contribution but would need further prospective evaluation. We agree with Dr. Vargas Lopez that salvage surgery is an important treatment option to consider for subsets of glioblastoma patients, especially those with large symptomatic lesions. However, we interpret the limited data to indicate that only patients who undergo gross total tumor resections are likely to derive a survival benefit.2,3 If only a subtotal reaction is possible, a reoperation is unlikely to benefit the patient in terms of improving survival. As Dr. Vargas Lopez indicates, there are retrospective series and meta-analyses suggesting potential benefit of surgery, but these all have limitations, including selection bias, and represent low-level evidence data. Despite the importance of this issue, randomized controlled studies or other high-quality studies to guide our practice have been very challenging to perform. We had also already indicated that the level of evidence for all other interventions, not only surgery, is low. As Dr. Vargas Lopez indicates, bevacizumab may affect wound healing and increase the risk of reoperation. If a patient requires surgery, then bevacizumab should indeed be withheld. However, for many patients who do not necessarily require immediate surgery, the rationale of holding bevacizumab to keep open the option of surgery could also deprive the patient of a treatment that could potentially improve their quality of life. Whether a patient undergoes a reoperation requires careful balancing of the potential risks and benefits, taking into account the tumor location, the extent of resection possible, need for tissue to guide treatment decision, the patient’s condition, prognosis and preference, and the availability of further therapy following surgery. It is a useful treatment option for many patients, but sometimes is also used excessively and inappropriately and needs to be considered in the context of the other available treatments. Multidisciplinary consensus is likely to serve the patient’s interest best in this setting. In the future, reoperation may play a greater role in the treatment of patients with recurrent glioblastomas as part of “window-of-opportunity” and “neoadjuvant” surgical trials, to administer novel therapies with poor penetration across the blood-brain barrier, or potentially to obtain tissue for analysis to guide further therapy.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.029
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0290.025
Insufficient payload (model declined to judge)0.0050.004

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 head0.025
GPT teacher head0.278
Teacher spread0.252 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

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Citations0
Published2020
Admission routes1
Has abstractno

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