Effects of food on pharmacokinetics of immediate release oral formulations of aspirin, dipyrone, paracetamol and NSAIDs – a systematic review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AIMS: It is common to advise that analgesics, and especially non-steroidal anti-inflammatory drugs (NSAIDs), be taken with food to reduce unwanted gastrointestinal adverse effects. The efficacy of single dose analgesics depends on producing high, early, plasma concentrations; food may interfere with this. This review sought evidence from single dose pharmacokinetic studies on the extent and timing of peak plasma concentrations of analgesic drugs in the fed and fasting states. METHODS: A systematic review of comparisons of oral analgesics in fed and fasting states published to October 2014 reporting kinetic parameters of bioavailability, time to maximum plasma concentration (tmax ), and its extent (Cmax ) was conducted. Delayed-release formulations were not included. RESULTS: Bioavailability was not different between fasted and fed states. Food typically delayed absorption for all drugs where the fasting tmax was less than 4 h. For the common analgesics (aspirin, diclofenac, ibuprofen, paracetamol) fed tmax was 1.30 to 2.80 times longer than fasted tmax . Cmax was typically reduced, with greater reduction seen with more rapid absorption (fed Cmax only 44-85% of the fasted Cmax for aspirin, diclofenac, ibuprofen and paracetamol). CONCLUSION: There is evidence that high, early plasma concentrations produces better early pain relief, better overall pain relief, longer lasting pain relief and lower rates of remedication. Taking analgesics with food may make them less effective, resulting in greater population exposure. It may be time to rethink research priorities and advice to professionals, patients and the public.
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.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.002 |
| 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it