A Quality and Completeness Assessment of Testicular Cancer Health Information on TikTok
Bibliographic record
Abstract
TikTok has become a hub for easily accessible medical information. However, the quality and completeness of this information for testicular cancer has not been examined. Our study aims to assess the quality and completeness of testicular cancer information on TikTok. A search was performed on TikTok using the search terms “Testicular Cancer” and “Testicle Cancer”. Inclusion criteria encompassed videos about testicular cancer in English. We excluded non-English videos, irrelevant videos, and videos without audio. We evaluated these videos using the DISCERN instrument and a completeness assessment. A total of 361 videos were considered for screening and 116 videos were included. Of these, 57 were created by healthcare professionals (HCPs). The median video length was 40 s (5–277 s), with >25 million cumulative views and a median of 446,400 views per video. The average DISCERN score was 29.0 ± 5.7, with HCPs providing higher-quality videos than non-HCPs (30.8 vs. 5.5, p < 0.05). HCPs also had more reliable videos (21.2 vs. 18.1, p < 0.05). Overall quality levels were mostly poor or very poor (97.4%), with none being good or excellent. Most HCP videos were poor (63.2%), whilst many non-HCP videos were very poor (61.0%). The most viewed video had 2,800,000 views but scored a 31 on the DISCERN tool and one on the completeness assessment. The highest DISCERN score had 11,700 views. HCP videos better defined the disease and were more complete (p < 0.05). Most videos discussed self-assessment but were lacking in definitions, risk factors, symptoms, evaluation, management, and outcomes. Most of TikTok’s testicular cancer information lacks quality and completeness, whilst higher-quality videos have limited reach.
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 | 0.019 | 0.110 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".