Bibliographic record
Abstract
W ikipedia defines information overload as "an excess amount of information being provided, making processing and absorbing tasks very difficult for the individual because sometimes we cannot see the validity behind the information … and the risk of misinformation.[It is] 'a symptom of the high-tech age, which is too much for one human being to absorb in an expanding world of people and technology.'" 1 Reasons for information overload are improved access to information, exploding amounts of new information, expanding mechanisms for duplication and dissemination of information, contradictions and inaccuracies in available information, and a perceived lack of a standard method for comparing and processing informationall of which ultimately lead to "information pollution" and a "low signal-to-noise ratio". 1 Pharmacists are viewed by the public and other health care professionals as health and drug information experts who can guide patients through the data minefield, providing unique insight, sound judgment, and practical perspective on the risks, benefits, and value of medications to prevent and treat diseases and improve patient care.However, it is becoming increasingly difficult to perform this task, as pharmacists are bombarded by mantras of "miracle" medications, the "lurking dangers" of newly discovered drug reactions and interactions, and flavourof-the-week lifestyle "makeovers".2 If we do not acknowledge the impact of this problem, apply proven, effective coping strategies, and investigate novel solutions to withstand the barrage of information, the very data upon which we rely to promote safe, effective, cost-conscious therapy will paralyze us.Today, drug information is more accessible than ever before, and massive amounts of information can be at our fingertips in seconds.A recent PubMed search on the term "drug" identified over 3.4 million article "hits".Limiting the search to English articles relating to humans revealed a still-overwhelming 5320 randomized controlled trials, 610 metaanalyses, and 165 clinical practice guidelines!However, this relatively high-quality information, which can be searched for and retrieved selectively in a controlled fashion, is not what is
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 | 0.034 | 0.208 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.013 | 0.023 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".