Bibliographic record
Abstract
Off-labelprescribing,whichisbroadlydefinedastheprescribing of drugs outside of the marketing authorization determined by a licensing body such as the US Food and Drug Administration, is a controversial issue. On the pro side, off-label prescribing allows individualclinicianstomaketheultimatedecisionastowhetheror notaparticularlicenseddrugisofpotentialbenefittoanindividual patient. There are a number of circumstances in which a physician may be compelled to prescribe off label, some of which may be considered more appropriate than others. Early use of an already licensed drug in a setting that is supported by data from a newly reported randomized study, but that has not yet been vetted through the drug approval process, may be an example of a more appropriateoff-labeluse.Thismaybeparticularlysalientwhenthe approval process is slow, thus limiting access of patients and providers to effective treatments in a timely manner. Additionally, for many cancers, notably rare tumors, there may never be enough evidencetosupportalabelingindicationbecauseoftheinabilityto conduct the appropriate trial as a result of inadequate patient numbers or lack of financial incentives. Not surprisingly, a previous survey found that American oncologists do discuss off-label use with their patients and feel comfortable prescribing for offlabel indications in some circumstances. 1
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 | 0.020 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.009 | 0.019 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.040 | 0.044 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".