Quality of DCIS information on the internet: a content analysis
Bibliographic record
Abstract
PURPOSE: Women with ductal carcinoma in situ (DCIS) experience lingering confusion and anxiety, and may use the Internet for supplemental information. This study assessed the content and quality of DCIS information on the Internet. METHODS: We searched Google for English-language, publicly available DCIS information tools published from 2010 to current by non-profit organizations. We summarized tool characteristics, DCIS labels, and information important to women with DCIS corresponding to domains of a patient-centred care (PCC) framework. Tool quality was appraised with the DISCERN instrument. RESULTS: Of 39 tools included, most were plain language summaries published since 2016. Tools employed a median of 2.0 labels (range 1.0 to 5.0) for DCIS, most frequently non-invasive breast cancer (29, 74.4%), abnormal cells (14, 35.9%), pre-cancer (14, 35.9%), and early form of breast cancer (13, 33.3%). Tools addressed a median of 4.0 (range 2.0 to 5.0) PCC domains. Few tools contained content in the domains of fostering the relationship (30.8%), addressing emotions (41.0%), or follow-up (41.0%); 74.4% noted the risk of progression or recurrence but provided vague details. Tools were assessed as high (25.6%), moderate (48.7%), and low (25.6%) quality. CONCLUSIONS: Few DCIS information tools available to women on the Internet meet quality criteria for consumer health information or address concerns of importance to women with DCIS. By identifying a range of poorly defined terms used to label DCIS, and specific content domains that were lacking, this study identified how existing tools could be improved, and identified higher-quality tools that clinicians can use when discussing DCIS with patients.
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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 | 0.033 | 0.146 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.031 | 0.028 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| 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".