Bibliographic record
Abstract
The process of variant identification by molecular technologies, through sequencing or array-based methods, is a complex process that requires protocols, quality controls, and technical and clinical knowledge. The complexity of the process does not end with the identification of a genomic variant, but molecular geneticists and clinicians must have sufficient knowledge and experience to interpret the finding and suggest the impact that this variant may or may not have on the patient.Interpretation is not an isolated exercise, but requires repetitive review and systematic review of the available literature on the variant in question. It also requires knowledge of epidemiology, statistics and research methodology to be able to establish a reasonable interpretation with the greatest possible certainty, which is especially important in a clinical setting. Mastering new computer resources such as variant databases (ClinVar, ClinGen, among others) and knowing the population frequency of that variant are additional skills necessary in the exercise of clinical molecular diagnosis. For this reason, in this issue we are pleased to present three clinical cases focused on molecular diagnostics: a case of Allan-Herndon-Dudley Syndrome, another on familial hypercholesterolemia, and finally, a third clinical case on Pitt-Hopkins Syndrome. The three present different approaches, perspectives, and conclusions, allowing us to see the methodologies already implemented in our region. We also present an article on pain and its genetic basis, putting into perspective the variability of perception of each individual to pain stimuli, which may depend on genetic, epigenetic and environmental factors.This issue shows our regional efforts to carry out molecular diagnostics and precision medicine, which are already a reality in our countries, and at the same time, we share knowledge and experiences, which will allow us to document the advances in this discipline of Genetics and Clinical Genomics. Once again, we thank you, our reader, for appreciating this group effort, and we invite you to participate in this academic exercise in our next issues. We hope you will once again enjoy this second issue we have prepared for you.
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 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 | 0.019 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.015 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.038 | 0.017 |
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".