Decision-making in Surgery or Active Surveillance for Low Risk Papillary Thyroid Cancer During the COVID-19 Pandemic
Bibliographic record
Abstract
We describe our experience conducting a prospective observational cohort study on the management of small, low risk papillary thyroid cancer during the COVID-19 pandemic. Our study participants are given the choice of active surveillance (AS) or surgery, and those in the AS arm are followed at the study center, whereas surgical patients undergo usual care. During the pandemic we have transitioned from in-person research patient visits to largely virtual care of patients under AS. As of 30 October 2020, we had enrolled 181 patients enrolled in our study (including 25 during the pandemic), of which 92.3% (167/181) consented to telephone communication and 79.0% (143/181) consented to secure videoconferencing communication. Prior to the pandemic, 74.5% (117/157) of our patients chose AS over surgery, whereas during the pandemic, 96.0% (24/25) chose AS. Of the 133 study patients who were under AS within the timeframe from 12 March 2020, to 30 October 2020, the percentage of patients who missed appointments was 8.3% (11/133, for neck ultrasound and physician visits, respectively) and delayed appointments was 23.3% (31/133). This preliminary data suggests that prospective observational research on AS of thyroid cancer can safely continue during the pandemic.
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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".