Industry–Contract Research Organization Pathology Interactions
Bibliographic record
Abstract
This article provides observations on the features of sponsor-contract research organization communication that will achieve the best quality pathology report based on our collective experience. Information on the test article and any anticipated findings should be provided, and initial slide examination should be done with knowledge of treatment group (but may be followed by blinded review of target tissues to determine no-effect levels). Only a pathologist should write or revise the pathology report or the pathology section of the overall study report. To address concerns related to undue sponsor influence, comments by sponsors should be presented as suggestions rather than directives. Adversity should be defined for each finding by the study pathologist, but the no-observed adverse effect level should not be discussed in the pathology report. Board-certified pathologists are recommended, but are not essential. Sponsors that have a particular format or report preferences should make them known well in advance. Histologic processing "to glass" of protocol-specified tissues from all dosage groups is recommended for rapid evaluation of target tissues. Telepathology is beneficial in certain situations, but it is usually more efficient for the study pathologist and reviewing pathologist to be in the same physical location to review differences of opinion and reach a consensus.
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 | 0.037 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.077 | 0.025 |
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".