Evidence of advanced stage colorectal cancer with longer diagnostic intervals: a pooled analysis of seven primary care cohorts comprising 11 720 patients in five countries
Bibliographic record
Abstract
BACKGROUND: The benefits from expedited diagnosis of symptomatic cancer are uncertain. We aimed to analyse the relationship between stage of colorectal cancer (CRC) and the primary and specialist care components of the diagnostic interval. METHODS: We identified seven independent data sets from population-based studies in Scotland, England, Canada, Denmark and Spain during 1997-2010 with a total of 11 720 newly diagnosed CRC patients, who had initially presented with symptoms to a primary care physician. Data were extracted from patient records, registries, audits and questionnaires, respectively. Data sets were required to hold information on dates in the diagnostic interval (defined as the time from the first presentation of symptoms in primary care until the date of diagnosis), symptoms at first presentation in primary care, route of referral, gender, age and histologically confirmed stage. We carried out reanalysis of all individual data sets and, using the same method, analysed a pooled individual patient data set. RESULTS: The association between intervals and stage was similar in the individual and combined data set. There was a statistically significant convex (∩-shaped) association between primary care interval and diagnosis of advanced (i.e., distant or regional) rather than localised CRC (P=0.004), with odds beginning to increase from the first day on and peaking at 90 days. For specialist care, we saw an opposite and statistically significant concave (∪-shaped) association, with a trough at 60 days, between the interval and diagnosis of advanced CRC (P<0.001). CONCLUSIONS: This study provides evidence that longer diagnostic intervals are associated with more advanced CRC. Furthermore, the study cannot define a specific 'safe' waiting time as the length of the primary care interval appears to have negative impact from day one.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".