Response: Re: Screening for Emotional Distress in Cancer Patients: A Systematic Review of Assessment Instruments
Bibliographic record
Abstract
We thank Garssen and van der Lee ( 1 ) for their interest in our review. They rightly point out that distress screening should have a scientific foundation upon which to build clinical applications. They describe potential problems and question the overall value of screening. We fully concur that screening is only in part a scientific issue. As noted by Garssen and van der Lee, distress screening is meant for clinical use ( 2 ), and they sensitize the reader to the difficulties in making screening useful for everyday clinical practice. The key objective of our review was to evaluate the science behind distress screening instruments so that future research on screening and clinical decision making can be based on empirically validated tools rather than on personal penchants. We believe that we have achieved that objective and never anticipated that our review would resolve the much broader question of how useful screening is. We are fully aware that our contribution is but one building block in the process of making screening maximally useful for clinical practice. We addressed the pros and cons of screening tool length and found that longer measures allow a more comprehensive assessment of emotional distress. These longer measures included distress questionnaires ( 3 , 4 ) that were developed specifically in cancer patients, and we recommended them on the basis of their good psychometric properties. These longer instruments also capture a wider range of domains, namely physical symptoms, specific disease-related fears, everyday life restrictions, information deficits, social strains, support needs, actual support, and decrements in quality of life. Garssen and van der Lee asked for a wider range of measures to be considered. However, it is important to recognize that these domains are not necessarily distinct from distress. Indeed, both anxiety and depressive symptoms were (independently) highly correlated with a cancer-specific screening instrument that measures cancer-related fears, psychosomatic complaints, everyday life restrictions, information deficits, and social strains ( r = .73 and r = .75) ( 3 ). Among the symptoms that cancer patients experience, depression is a persistent problem and predicts cancer mortality independent of biological markers ( 5 ). Screening for depression may be of particular value early in the disease process. We agree with Garssen and van der Lee that patient concerns shift over time and that uncertainty, unsolved information needs, and management of pain, fatigue, and poor sleep become salient later in the disease trajectory and should be assessed accordingly at these times. However, we do not agree with Garssen and van der Lee that psychosocial counseling should be based on patient-reported needs because not all patients with severe psychological symptoms seek help on their own and some may benefit from a health professional's proactive referral. Regarding the much broader question of clinical usefulness of psychological screening, routine screening in British Columbia cancer centers revealed a change in patient mix and in more referrals of men and ethnic minority groups ( 5 ), a trend that opens the door for more equal access to psycho-oncological counseling. What remains is the problem of poor uptake of psycho-oncological counseling services that Garssen and van der Lee so well describe. In this regard, we suspect that they would join ranks with us in a call for an entire research agenda on the topic of poor uptake.
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 | 0.025 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".