Primary Care Referrals for Suspected Hematologic Malignancies: Incidence, Factors Affecting Choice of Specialist, and Flow of Referral Information.
Bibliographic record
Abstract
Abstract Abstract 3830 Background: Although the primary care physician (PCP) is often the first point of contact for patients with suspected hematologic malignancy, little is known about hematologic referrals from primary care, including their frequency, the factors that affect choice of specialist, and the quality of information exchanged. Methods: In April 2010, we administered a 34-item questionnaire to a random sample of 190 physicians in the state of Massachusetts identified as PCPs (family practice, general practice, or internal medicine) in the American Medical Association's physician file. PCPs were given the opportunity to complete the survey via post or Internet. An additional mailing was sent to non-respondents, followed by at least two attempts at telephone contact. Physicians were asked for the approximate number of patients seen in the past year with suspected hematologic malignancy, the frequency of formal specialty referral, and informal “curbside” referral. PCPs were also queried about the factors that influence their choice of specialist, and about the information exchange with the specialist; these measures were then analyzed by self-reported PCP characteristics using chi-square statistics. Results: As of August, 2010, 118 physicians had responded (response rate = 62.1%). 67.8% identified themselves as internists, and 61.9% were male. The median reported patient panel size during the prior 12 months was 1800; median percentage of patients ≥ 65 years was 30.0%; median percentage of patients in managed care was 55.0%; and median year of graduation from residency, 1996. PCPs were evenly distributed with respect to academic affiliation (from no affiliation to full-time faculty). The median number (IQR) of patients in the prior 12 months who were suspected of having hematologic malignancy was 5 (3, 10). Among suspected hematologic malignancies, the median number formally referred to a specialist (hematologist or surgeon) was 5 (3, 10), and the median number who received informal “curbside” consult was 0 (0, 0.5). Respondents rated the importance of several factors in their choice of specialist (1 = not important at all to 5 = extremely important). Those factors rated ≥ 3 included reputation of specialist/facility (94.9%), patient's preference for site of care (92.4%), distance of site from patient's home (89.8%), specialist's affiliation with a cancer center (88.1%), practice's affiliation with specialist (82.2%), personal relationship with specialist (79.7%), patient's ability to pay (67.0%), and availability of clinical trials at the referral site (63.6%). The following table summarizes responses to questions about flow of referral information and follow-up: Conclusions: Consultation for suspected hematologic malignancy from PCPs is relatively infrequent, tends to manifest through formal referral as opposed to informal discussion, and is most often affected by specialist reputation and patient preference for site of care. Only about half of our respondents reported providing the specialist with a referral letter or email, which may result in poor quality of referral information. Alternately, a high number reported giving a copy of abnormal test results to their patients to bring to the specialist, which may ameliorate this issue and reflect an ongoing evolution in the patient/provider partnership. Moreover, fairly often, patients have not been to see the specialist upon follow-up with their PCP. This finding seems to reflect patient cancellations rather than a failure in physician systems, suggesting that increases in patient education and personalized follow-up may be the best approach to ensure completion of timely hematologic referrals. Disclosures: No relevant conflicts of interest to declare.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".