Bibliographic record
Abstract
Due to various activities—including those by the World Bank and the Bill & Melinda Gates Foundation—we are happy to see a global decrease in malaria cases. But too many still suffer and die from malaria so malaria prevention or at least to prevent deaths by malaria is of paramount importance, still. And this is not true for the indigenous population only but for travellers as well. Balancing the risk of disease with that of possible side effects of chemoprophylaxis is a difficult task which mostly leads to split decisions even in experts (as was shown in a thrilling Pro-Con debate at the CISTM14 in Quebec in May 2015). This lack of guidance left and still leaves many colleagues (including qualified travel medicine practitioners) quite puzzled—not to talk about those on target—i.e. the travellers. For sustainable guidance my deep believe is: KEEP IT SIMPLE—otherwise counselling doctors (not all of whom are experts in tropical medicine or malariology) as well as travellers will get lost in confusion for sure1 (‘The compliance is inversely proportional to the complexity of the prescription’—Haynes and Sackett 1976). Insect bite precautions (IBP) from dusk until dawn are the mainstay of malaria prophylaxis—so this is of paramount importance for each traveller going to malaria endemic areas no matter how high the risk actually is. The first crucial decision of the binary decision tree is the definition of high risk of exposure (but may consider high risk of complications in vulnerable travellers as well). Whereas some sources say that there is no method of quantifying the risk2 in other sources high risk is defined by a risk …
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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".