Response to letter regarding “Assessing the association between preoperative neurocognitive status and IDH1 mutation status in high-grade gliomas: A deeper look into potential confounding variables.”
Bibliographic record
Abstract
Thank you for the opportunity to respond to the letter from Dr. Waqar Khan regarding our paper titled “Assessing the association between preoperative neurocognitive status and IDH1 mutation status in high-grade gliomas.”1 Our study indicates that IDH1—wild-type HGG patients present greater neurocognitive impairment, in executive functions particularly, compared to IDH1-mutant ones. After adjusting for well-known factors, we support that tumor growth kinetics may play a more profound role than other parameters in neurocognitive status of HGG patients. We appreciate authors’ interest in our study, especially with regard to their suggestion for a deeper look into potential confounding variables. Firstly, Dr. Aimen Wagar Khan highlights the need for a larger sample size and for a multi-centered study design for more reliable and generalizable results. We agree that bigger studies are more powerful by providing a large sample size that allows generalizability. Nevertheless, there are occasions that while results of local single-center studies support a hypothesis, larger multi-centered studies fail to do so questioning thus if the bigger is always the better.2 Multi-centered design may come with disadvantages such as heterogeneity in methods and patients’ characteristics. Our study design limited inter-rater reliability bias as diagnostic/intervention techniques were all performed by one medical team of each specialty. In parallel, our sample (n = 147) is similar to the ones employed by consistent with our results studies.3 Conclusively, without aiming to compromise the value of multi-centered studies, we would like to highlight that single-center studies are also of great importance when they are well designed. Secondly, authors point out as a potential confounder the fact that most of our patients were on corticosteroids at the time of cognitive assessment. In particular, they make a reference to a study4 showing, according to Dr. Wagar Khan, that diminished executive functions are linked to corticosteroids in patients with recurrent glioblastoma. However, going through a thorough reading of the aforementioned study, one would notice that only memory and not executive functions (our cognitive measurement) were significantly associated with corticosteroid intake. In general, most data on corticosteroids’ adverse cognitive effects comes from patients with systematic conditions as opposed to brain tumors. Regarding gliomas, there are only few studies with contradicting results on corticosteroids’ effect on cognition.5 Finally, the fact that the majority of our patients in each subgroup were on corticosteroids does not enable us to hypothesize that our differences in cognitive status between IDH-wild type and mutant one patient were due to steroids. Finally, authors point out as a confounding factor potential vascular comorbidities/diabetes mellitus patients may have suffered from. Indeed, vascular disorders such as diabetes mellitus may are associated with poor cognitive function in general—older than our clinical sample—population, however.6 Regarding gliomas, meta-analyses support that patients with diabetes mellitus present a decreased risk for developing gliomas7 indicating that glioma cohorts may have decreased risk for diabetes mellitus. Nevertheless, further studies are warranted to explore the effect of vascular comorbidities on cognition in glioma patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.014 | 0.011 |
| Insufficient payload (model declined to judge) | 0.038 | 0.027 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".