Assessing the impact of the COVID-19 pandemic on patients referred to a lung cancer rapid assessment clinic in Ontario, Canada
Bibliographic record
Abstract
RATIONALE The COVID-19 pandemic negatively impacted lung cancer (LC) outcomes. The Lung Diagnostic Assessment Program (LDAP) in Southeastern Ontario is a rapid assessment clinic for patients with suspected LC.OBJECTIVE Characterize the impact of COVID-19 on presentations and timeliness of care within LDAP.METHODS Retrospective study of LDAP-referred patients pre-COVID-19 (December 2019–March 2020) with matched cohorts post-COVID-19 Year 1 (December 2020–March 2021), and Year 2 (December 2021–March 2022). Data included: patient demographics, LC diagnosis, stage, timeliness of care and symptoms. Statistical analysis included unpaired t-tests and chi-squared tests.MEASUREMENTS AND MAIN RESULTS We reviewed 273 patients pre-COVID-19, 287 post-COVID-19 Year 1 and 218 Year 2. Post-COVID-19, more patients were suspected to have cancer at referral (pre-COVID-19, 28.6% vs. post-COVID-19 Year 2, 37.2%, p = 0.03), and more patients did not attend consultation (9.8%, 10.7% vs 1.0% pre-COVID-19, p = 0.0002) due to hospitalization or declining assessment. Average time from referral to consultation was longer during post-COVID-19 Year 1 (12.2 vs. 17.0 days, p = 0.003) but unchanged by Year 2; times from referral to diagnosis and treatment were unchanged across cohorts (42.7 vs. 44.1 vs. 40.9, p = NS) and (74.0 vs. 66.2, vs. 70.3, p = NS, respectively). Post-COVID-19 patients reported longer symptom duration (5.0 vs. 3.5 months, p = 0.04).CONCLUSIONS During the COVID-19 pandemic, patient characteristics and timeliness of care within LDAP were unchanged, but more patients experienced clinical decline and longer symptom duration prior to consultation. While care within LDAP was relatively insulated, barriers to care prior to referral may have contributed to later presentation.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".