MétaCan
Menu
Back to cohort

Predicting Progression of Oral Dysplasia—Response

2013· letter· en· W2164391460 on OpenAlex
Miriam P. Rosin, Lewei Zhang, Li Mao

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCancer Prevention Research · 2013
Typeletter
Languageen
FieldDentistry
TopicOral Health Pathology and Treatment
Canadian institutionsBC Cancer AgencySimon Fraser UniversityUniversity of British Columbia
FundersNational Institute of Dental and Craniofacial Research
KeywordsProtocol (science)Interpretation (philosophy)Computer sciencePsychologyMedicinePathology

Abstract

fetched live from OpenAlex

We thank Drs. Gomes, Fonseca-Silva, and Gomez for their comments on our recently published article (1). That article validated a LOH risk model for use in differentiating between high-risk and low-risk oral dysplasias—a critical barrier in selecting patients for advanced oral cancer preventive intervention. We agree with Gomes and colleagues that there is a need to build on this finding and to develop a clinical tool that is accessible to a broad range of users. However, we stress caution in the way in which this evolution in technology occurs and the need to ensure that changes in protocol result in a new technology that has similar (or improved) capacity to predict outcome for such lesions.The data shown by Gomes and colleagues illustrate some of the inherent difficulties that can occur when making a transition between different platforms as biomarkers evolve. Our protocol used a “radiation-labeling visual inspection” of PCR products separated on polyacrylamide gels. Gomes and colleagues propose the evolution to a “dye-labeling intensity comparison” with samples separated by capillary electrophoresis. It is important to note that the interpretation of data can be subjective in both systems and each will have its own limitations and biases, with experience of the user being important. For example, the primer set used in Fig. 1A resulted in the 2 bands differing widely in intensity on polyacrylamide gels; in our opinion, not ideal for gel electrophoresis. It was scored by Gomes and colleagues as negative by visual inspection, but we suggest that there seems to be some imbalance in the allele patterns. With an increase in exposure time, the imbalance may become more apparent and be scored as a loss. Figure 1B points to a sample showing somewhat weaker intensity of the upper band compared with the lower band in the gel analysis. This might qualify for LOH if the upper band was better resolved. The authors score this sample as negative for LOH in the capillary electrophoresis system based on deviation from a chosen cutoff value for differences in intensities of the 2 alleles in control and test samples. The choice of cutoff values for different primer sets affects the sensitivity of the gene scan to distinguish low levels of change in LOH. These levels cannot be arbitrarily chosen but need to be set and then validated clinically. The authors also consider capillary electrophoresis to be more sensitive than gel electrophoresis. It is important to consider the possibility that higher sensitivity of detection may not always result in an improvement in prediction of outcome. In this case, increasing sensitivity might detect smaller numbers of cells with LOH rather than a clone (many cells with LOH). The capacity to clonally expand could matter.There are other possibilities that could be considered as next steps, many of them cutting edge, such as the development of a “single molecule–based” quantitative analysis using next sequencing technologies such as the Ion Torrent Platform, which would produce a call out of number of gene copies (2). We suggest that the choice for technologic change requires a careful comparative study between platforms to look for similarities and differences in the LOH calls, to determine the relative ability to detect LOH and its clinical relevance. To that end, the procedure used in our article represents the only system to be validated prospectively for association with oral cancer risk. As such it represents the validated standard by which other systems in the future could be compared for association with outcome. Perhaps such an approach should be accepted to determine the next evolution of a device—much like drug studies are run—with a validated drug shown to affect outcome being used as the comparative arm in trials using new drugs, to determine if outcome is improved.The best situation would be for the development of such formal studies with new technologies to be collaborative efforts that would lead to a universal tool that would be broadly adopted instead of a divergence into many “tools” without appropriate validation. The latter course, with multiple tools, means losing the ability to directly compare data from different populations and in different settings between laboratories. The time to develop that universal tool is now, before we lose both the opportunity and the momentum.See the original Letter to the Editor, p. 614No potential conflicts of interest were disclosed.

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.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.534
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.153
GPT teacher head0.516
Teacher spread0.362 · 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