Bibliographic record
Abstract
Background: Cancer can be diagnosed and treated earlier by reducing time to diagnosis. Awareness interventions may shorten relevant time intervals and lead to earlier presentation and diagnosis. However, reported definitions are often ambiguous, limiting the opportunity for robust comparison. The Aarhus Statement (1) provides a checklist, guidance and clear definitions for these time intervals for cancer researchers. Research question: To compare published definitions of time intervals in the patient diagnostic pathway against those recommended by the Aarhus statement. Method: Two systematic reviews were carried out identifying literature on early diagnosis pathways and interventions in colorectal and ovarian cancers (343 and 132 papers assessed respectively). Worldwide, English language studies up to April 2013 were identified in PubMed and Embase. For each cancer, interval definitions from papers rated medium or strong quality were extracted and categorised against the most appropriate Aarhus interval categories: patient, doctor, primary care, system, secondary care, diagnostic, treatment and total intervals. Intervals that were ambiguous and/or did not completely match the Aarhus definitions were highlighted. Results: Interval definitions were categorised from 78 colorectal and 21 ovarian cancer papers: 208 and 85 definitions were extracted from each cancer type respectively. Of these definitions, 77% (colorectal) and 75% (ovarian) did not completely fit any of the Aarhus definitions. Inconsistencies ranged from the intervals being undefined, ill-defined, differences in definitions of start and end points (e.g. first symptom, first presentation/ clinical appearance, referral, secondary care, diagnosis and treatment), restriction of patients included and overlapping time intervals.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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 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".