The impact of concordance with a lung cancer diagnosis pathway guideline on treatment access in patients with stage IV lung cancer
Bibliographic record
Abstract
Background: Timely access to treatment of lung cancer is dependent on efficient and appropriate patient assessment and early referral for diagnostic workup. This study assesses the impact of Cancer Care Ontario (CCO) Lung Cancer Diagnostic Pathway Guideline (LCDPG) concordance on access to treatment of stage IV lung cancer patients referred to the Diagnostic Assessment Program (DAP) at a Canadian tertiary cancer centre. Methods: This retrospective cohort study includes patients diagnosed with clinical stage IV lung cancer referred to the DAP at a Canadian tertiary cancer centre between November 1, 2015 and May 31, 2017. Referral concordance was determined based on CCO LCDPG. The primary outcome; time to treatment from initial healthcare presentation; was compared between the concordant and discordant referrals. Results: Two hundred patients were referred for clinical stage IV lung cancer during the study period. Of these referrals, 151 (75.5%) were assessed and referred in concordance with LCDPG. Guideline concordant referrals were associated with reduced time to treatment from first healthcare presentation compared with guideline discordant referrals (55.3 vs. 108.8 days, P<0.001). Time to diagnostic procedure (32.2 vs. 86.7 days, P<0.001) and decision to treat (38.5 vs. 93.8 days, P<0.001) were also reduced with guideline concordance. The most common reason for discordant assessment and referral was delayed or inadequate investigation of symptoms in a high risk patient (32.7% of discordant referrals). Conclusions: Guideline concordant assessment and referral of stage IV lung cancer patients results in reduced time to diagnosis and treatment. Future research and education should focus on improving factors that delay DAP referral.
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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 | 0.002 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".