Bibliographic record
Abstract
BACKGROUND: Since the advent of ivermectin (along with melarsomine and doxycycline), heartworm has come to be viewed as a solved problem in veterinary medicine, diminishing investment into non-clinical research on Dirofilaria immitis. However, heartworm infections continue to pose problems for practitioners and their patients and seem to be increasing in frequency and geographic distribution. Resistance to preventative therapies (macrocyclic lactones) complicates the picture. The use of chemotherapy for other kinds of pathogens has benefitted enormously from research into the basic biology of the pathogen and on the host-pathogen interface. A lack of basic information on heartworms as parasites and how they interact with permissive and non-permissive hosts greatly limits the ability to discover new ways to prevent and treat heartworm disease. Recent advances in technical platforms will help overcome the intrinsic barriers that hamper research on D. immitis, most notably, the need for experimentally infected dogs to maintain the life cycle and provide material for experiments. Impressive achievements in the development of laboratory animal models for D. immitis will enhance efforts to discover new drugs for prevention or treatment, to characterize new diagnostic biomarkers and to identify key parasite-derived molecules that are essential for survival in permissive hosts, providing a rational basis for vaccine discovery. A 'genomics toolbox' for D. immitis could enable unprecedented insight into the negotiations between host and parasite that enable survival in a permissive host. The more we know about the pathogen and how it manipulates its host, the better able we will be to protect companion animals far into the future.
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 | 0.029 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.010 | 0.023 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.024 | 0.008 |
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".