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Record W1973986864 · doi:10.1093/neuonc/nov022

Response to "The epidemiology of glioma in adults: a 'state of the science' review"

2015· letter· en· W1973986864 on OpenAlexaff
Quinn T. Ostrom, Luc Bauchet, Faith G. Davis, Isabelle Deltour, James L. Fisher, Chelsea E. Langer, Melike Pekmezci, Judith Schwartzbaum, Michelle C. Turner, Kyle M. Walsh, M. R. Wrensch, Jill S. Barnholtz‐Sloan

Bibliographic record

VenueNeuro-Oncology · 2015
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicElectromagnetic Fields and Biological Effects
Canadian institutionsUniversity of Ottawa
FundersNational Cancer InstituteWorld Health Organization
KeywordsInternational agencyIncidence (geometry)GliomaPopulationDanishEpidemiologyMedicineCancerDemographyPathologyInternal medicineEnvironmental healthSociologyCancer researchMathematics

Abstract

fetched live from OpenAlex

Mr. Morgan's letter gives us the opportunity to clarify a number of points from our review, but we also need to correct one error. First, the letter incorrectly stated the funding source of the study by Deltour et al,1 which was entirely funded by the Danish Strategic Research Council, under grant 2064-04-0010. Second, in the section entitled “Nonionizing Radiation: Cellular Phones,” we focus on articles written since the publication of the International Agency for Research on Cancer (IARC) monograph3,4 which reviewed studies published before 2011. In recent studies since 2011, effect sizes are null, very small or very big, highlighting the complexity of brain tumor research (especially with respect to rapidly changing cellular phone technology.). Third, the interpretation of malignant brain tumor incidence rates is straightforward as long as they remain stable over time. Explanations of changes, however, can only be tentative. We respectfully disagree that data completeness affects the results of the studies presented for assessing general incidence trends of malignant brain tumors. For example, the Nordic cancer registries are considered models of completeness, with 93%–98% complete population ascertainment for malignant tumors in people younger than 70. A recent analysis of cancer registry data covering ∼98% of the US population from 2000–2010 showed decreased incidence of malignant brain tumors along with decreased incidence of some glioma subtypes.5 This data, together with the other incidence studies,1 suggests longer induction periods than currently investigated, lower risks than reported from some case-control studies, or the absence of any association. Decreases in incidence rates, as well as increases, may be a reflection of improved classification of tumors, evolution of medical practices, improved access to imaging or other technological changes, among numerous other factors, together with potential changes in other etiological factors. Some studies using cancer registry data showed an increase in glioma incidence from approximately 1975–1985, likely an artifact of increased detection from increased use of CT scans and MRIs over that period and improvements in cancer registration. All of these factors would have the greatest effect on reported incidence of nonmalignant tumors, while the majority of gliomas are malignant tumors. Fourth, one of the major weaknesses of cellular phone studies has been the lack of accurate and complete measurement of use.6 Although many investigations have compared self-reported use to information from cellular phone records to assess the magnitude of the reporting errors,2,7,8 Hardell and colleagues have not provided information on the potential role of recall errors in their studies. Recall bias may cause cases to artificially report higher past usage than controls, which could result in a false association between cellular phone use and brain tumors. Many of these studies have also been plagued with low participation rates, time delay in recruiting controls versus cases, and other methodological issues which may affect results. Several studies currently underway–such as COSMOS,2 MOBI-Kids,9 and GERoNiMO10–may resolve some of the methodological issues that have complicated the interpretation of previous results, by recruiting a very large cohort with prospective recording of phone use via cell phone operators, by using sophisticated phone apps to record number and duration of calls, laterality, hands-free/speaker phone use, etc., or by looking at this exposure in combination with other environmental exposures and incorporating biological mechanisms. Regardless of these improvements, accurate and complete exposure assessment for cellular phone use will likely remain very challenging for several reasons. Types of phones available vary significantly by time and location. There is significant variability in how phones are used (holding phone to head, side phone is used on, using speaker phone, or ear buds) between and within users, and these use patterns may vary over time. In summary, exposure assessment for cellular phone use is extremely complex, due to difficultly identifying dose (total, duration, or other measures) and the rapid changes in cellular phone technology. The recent evidence, with all of the weaknesses noted above, does not strengthen the evidence for an association between cellular phone use and occurrence of brain tumors.

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)0.010
metaresearch head score (Gemma)0.069
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 score0.026
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.069
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0030.002
Science and technology studies0.0020.003
Scholarly communication0.0040.005
Open science0.0040.004
Research integrity0.0260.025
Insufficient payload (model declined to judge)0.0140.009

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 head0.307
Teacher spread0.287 · 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".

Quick stats

Citations42
Published2015
Admission routes1
Has abstractyes

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