Health Card Retrieval for Consumer Health Search
Bibliographic record
Abstract
This paper investigates methods to rank health cards, a domain-specific type of entity cards, for consumer health search (CHS) queries. A key challenge in this context is which card(s) should be presented to the user. In particular, little evidence exists to determine the effectiveness of retrieval and ranking methods for health cards in CHS. CHS is a challenging domain, where users lack domain expertise and thus are often unable to formulate effective queries, and to interpret the retrieved results. In addition, unlike in other contexts, CHS presents the opportunity to exploit a number of domain specific characteristics and features. In this paper, we focus on difficult queries with self-diagnosis intents. Our study makes the following contributions: (1) it assembles and releases the first test collection of health cards for research purposes, and (2) it empirically evaluates a large range of entity retrieval methods adapted to health cards retrieval, including features specific to health cards for learning to rank. This is the first study that thoroughly investigates methods to rank health cards.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".