CINECA Curation-support application for care planning D5.4
Bibliographic record
Abstract
This deliverable aims at designing and evaluating a curation support system to help clinicians to decide which treatments should be considered given a set of somatic/germline mutations and some particular clinical conditions (e.g. diagnosis). It builds on top of previous deliverables from WP5, in particular D5.1 [1], as well as on the services delivered by WP1-3, such as the WP1 dataset search services and WP3 content normalization services. In the use-case scenario described in D5.1, the end-user was first invited to select some datasets. User queries are then distributed across a federated Beacon network, comprising data from UKBB (EMBL-EBI), CoLaus (Switzerland), H3 (South Africa) and CHILD (Canada). Beyond that, the search pipeline now integrates subtask T5.3.2 (Scoring service to assess pathogenicity scales of variants) directly as an information displayed to the user, but also as a feature of the treatment recommendation function. The scoring and care planning services are evaluated based on various scoring functions (e.g., cohort distributions, SIFT, Polyphen, literature counts). The evaluation shows a moderate association between the pathogenetic scoring proposed by T5.3.2 and the baseline scores selected for comparison. We also report on the efficiency of the pipeline.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.108 | 0.062 |
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".